2025-10-28 13:48:07.417748: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 13:48:07.428938: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1761655687.442667 2075027 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761655687.447084 2075027 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761655687.457631 2075027 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761655687.457654 2075027 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761655687.457658 2075027 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761655687.457660 2075027 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 13:48:07.460991: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/tune/impl/tuner_internal.py:144: RayDeprecationWarning: The `RunConfig` class should be imported from `ray.tune` when passing it to the Tuner. Please update your imports. See this issue for more context and migration options: https://github.com/ray-project/ray/issues/49454. Disable these warnings by setting the environment variable: RAY_TRAIN_ENABLE_V2_MIGRATION_WARNINGS=0
  _log_deprecation_warning(
2025-10-28 13:48:10,650	INFO worker.py:1927 -- Started a local Ray instance.
2025-10-28 13:48:11,368	INFO tune.py:253 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `Tuner(...)`.
2025-10-28 13:48:11,448	INFO trial.py:182 -- Creating a new dirname dir_5c837_8abd because trial dirname 'dir_5c837' already exists.
2025-10-28 13:48:11,451	INFO trial.py:182 -- Creating a new dirname dir_5c837_816e because trial dirname 'dir_5c837' already exists.
2025-10-28 13:48:11,453	INFO trial.py:182 -- Creating a new dirname dir_5c837_b88d because trial dirname 'dir_5c837' already exists.
2025-10-28 13:48:11,455	INFO trial.py:182 -- Creating a new dirname dir_5c837_0207 because trial dirname 'dir_5c837' already exists.
2025-10-28 13:48:11,457	INFO trial.py:182 -- Creating a new dirname dir_5c837_17cb because trial dirname 'dir_5c837' already exists.
2025-10-28 13:48:11,459	INFO trial.py:182 -- Creating a new dirname dir_5c837_d0ec because trial dirname 'dir_5c837' already exists.
2025-10-28 13:48:11,461	INFO trial.py:182 -- Creating a new dirname dir_5c837_bbcb because trial dirname 'dir_5c837' already exists.
2025-10-28 13:48:11,464	INFO trial.py:182 -- Creating a new dirname dir_5c837_2254 because trial dirname 'dir_5c837' already exists.
2025-10-28 13:48:11,466	INFO trial.py:182 -- Creating a new dirname dir_5c837_dc06 because trial dirname 'dir_5c837' already exists.
2025-10-28 13:48:11,468	INFO trial.py:182 -- Creating a new dirname dir_5c837_f246 because trial dirname 'dir_5c837' already exists.
2025-10-28 13:48:11,473	INFO trial.py:182 -- Creating a new dirname dir_5c837_90ad because trial dirname 'dir_5c837' already exists.
2025-10-28 13:48:11,476	INFO trial.py:182 -- Creating a new dirname dir_5c837_6c38 because trial dirname 'dir_5c837' already exists.
2025-10-28 13:48:11,480	INFO trial.py:182 -- Creating a new dirname dir_5c837_e992 because trial dirname 'dir_5c837' already exists.
2025-10-28 13:48:11,483	INFO trial.py:182 -- Creating a new dirname dir_5c837_de6b because trial dirname 'dir_5c837' already exists.
2025-10-28 13:48:11,486	INFO trial.py:182 -- Creating a new dirname dir_5c837_ecd7 because trial dirname 'dir_5c837' already exists.
2025-10-28 13:48:11,490	INFO trial.py:182 -- Creating a new dirname dir_5c837_62be because trial dirname 'dir_5c837' already exists.
2025-10-28 13:48:11,497	INFO trial.py:182 -- Creating a new dirname dir_5c837_eacf because trial dirname 'dir_5c837' already exists.
2025-10-28 13:48:11,502	INFO trial.py:182 -- Creating a new dirname dir_5c837_e4d8 because trial dirname 'dir_5c837' already exists.
2025-10-28 13:48:11,507	INFO trial.py:182 -- Creating a new dirname dir_5c837_c732 because trial dirname 'dir_5c837' already exists.
1 GPU(s) detected and VRAM set to crossover mode..
Se lanza la búsqueda de hiperparámetros óptimos del modelo
╭─────────────────────────────────────────────────────────────────╮
│ Configuration for experiment     ESANN_hyperparameters_tuning   │
├─────────────────────────────────────────────────────────────────┤
│ Search algorithm                 BasicVariantGenerator          │
│ Scheduler                        AsyncHyperBandScheduler        │
│ Number of trials                 20                             │
╰─────────────────────────────────────────────────────────────────╯

View detailed results here: /mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_C/case_C_ESANN_acc_gyr_17_classes/ESANN_hyperparameters_tuning
To visualize your results with TensorBoard, run: `tensorboard --logdir /tmp/ray/session_2025-10-28_13-48-09_926940_2075027/artifacts/2025-10-28_13-48-11/ESANN_hyperparameters_tuning/driver_artifacts`

Trial status: 20 PENDING
Current time: 2025-10-28 13:48:11. Total running time: 0s
Logical resource usage: 16.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_5c837    PENDING            2   adam            relu                                  128                 32                  5          0.000203836        127 │
│ trial_5c837    PENDING            3   adam            relu                                   32                 32                  5          0.00208772         128 │
│ trial_5c837    PENDING            3   rmsprop         tanh                                   32                 64                  3          0.00128416         116 │
│ trial_5c837    PENDING            3   rmsprop         relu                                  128                 32                  3          0.000155082         64 │
│ trial_5c837    PENDING            2   rmsprop         tanh                                   64                128                  3          0.000337639         72 │
│ trial_5c837    PENDING            2   adam            tanh                                   64                 64                  3          0.0016925          119 │
│ trial_5c837    PENDING            2   adam            tanh                                   64                 64                  3          0.000425983         69 │
│ trial_5c837    PENDING            4   adam            tanh                                  128                 32                  3          3.88891e-05        139 │
│ trial_5c837    PENDING            2   adam            relu                                   64                 64                  5          0.000136214        110 │
│ trial_5c837    PENDING            3   rmsprop         tanh                                   64                 64                  3          7.35601e-05        147 │
│ trial_5c837    PENDING            3   rmsprop         relu                                   32                 64                  5          0.00284314         125 │
│ trial_5c837    PENDING            4   rmsprop         tanh                                   32                 32                  3          0.00284271         103 │
│ trial_5c837    PENDING            3   rmsprop         relu                                  128                128                  3          0.00123868          97 │
│ trial_5c837    PENDING            4   rmsprop         relu                                  128                 32                  5          1.39284e-05         80 │
│ trial_5c837    PENDING            4   adam            relu                                   64                 32                  3          0.000166824         57 │
│ trial_5c837    PENDING            2   adam            tanh                                   32                 64                  5          0.00326602          91 │
│ trial_5c837    PENDING            4   adam            tanh                                   64                 32                  5          0.000133042        102 │
│ trial_5c837    PENDING            2   rmsprop         tanh                                   64                128                  3          0.00419881         110 │
│ trial_5c837    PENDING            4   adam            tanh                                   32                 32                  3          0.00235056         142 │
│ trial_5c837    PENDING            3   rmsprop         tanh                                   32                 32                  3          5.54287e-05        123 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_5c837 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_5c837 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                            57 │
│ funcion_activacion              relu │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00017 │
╰──────────────────────────────────────╯
Trial trial_5c837 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_5c837 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                           139 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00004 │
╰──────────────────────────────────────╯
Trial trial_5c837 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_5c837 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                           128 │
│ funcion_activacion              relu │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00209 │
╰──────────────────────────────────────╯
Trial trial_5c837 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_5c837 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                           103 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00284 │
╰──────────────────────────────────────╯
Trial trial_5c837 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_5c837 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                           142 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00235 │
╰──────────────────────────────────────╯
Trial trial_5c837 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_5c837 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                           119 │
│ funcion_activacion              tanh │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00169 │
╰──────────────────────────────────────╯
Trial trial_5c837 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_5c837 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                           110 │
│ funcion_activacion              relu │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00014 │
╰──────────────────────────────────────╯
Trial trial_5c837 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_5c837 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            69 │
│ funcion_activacion              tanh │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00043 │
╰──────────────────────────────────────╯
Trial trial_5c837 started with configuration:
╭─────────────────────────────────────╮
│ Trial trial_5c837 config            │
├─────────────────────────────────────┤
│ N_capas                           2 │
│ epochs                          127 │
│ funcion_activacion             relu │
│ numero_filtros                   32 │
│ optimizador                    adam │
│ tamanho_filtro                    5 │
│ tamanho_minilote                128 │
│ tasa_aprendizaje             0.0002 │
╰─────────────────────────────────────╯
Trial trial_5c837 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_5c837 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                           123 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00006 │
╰──────────────────────────────────────╯
Trial trial_5c837 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_5c837 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                           110 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje              0.0042 │
╰──────────────────────────────────────╯
Trial trial_5c837 started with configuration:
[36m(train_cnn_ray_tune pid=2076667)[0m 2025-10-28 13:48:14.642714: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
[36m(train_cnn_ray_tune pid=2076668)[0m 2025-10-28 13:48:14.649087: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
[36m(train_cnn_ray_tune pid=2076669)[0m WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
[36m(train_cnn_ray_tune pid=2076669)[0m E0000 00:00:1761655694.777160 2077818 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
[36m(train_cnn_ray_tune pid=2076645)[0m E0000 00:00:1761655694.720290 2077797 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
[36m(train_cnn_ray_tune pid=2076645)[0m W0000 00:00:1761655694.741854 2077797 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
[36m(train_cnn_ray_tune pid=2076645)[0m W0000 00:00:1761655694.741887 2077797 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
[36m(train_cnn_ray_tune pid=2076645)[0m W0000 00:00:1761655694.741890 2077797 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
[36m(train_cnn_ray_tune pid=2076645)[0m W0000 00:00:1761655694.741893 2077797 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
[36m(train_cnn_ray_tune pid=2076645)[0m 2025-10-28 13:48:14.747689: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
[36m(train_cnn_ray_tune pid=2076645)[0m To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
[36m(train_cnn_ray_tune pid=2076638)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
[36m(train_cnn_ray_tune pid=2076638)[0m   warnings.warn(
[36m(train_cnn_ray_tune pid=2076638)[0m 2025-10-28 13:48:17.982608: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
[36m(train_cnn_ray_tune pid=2076638)[0m 2025-10-28 13:48:17.982656: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:167] env: CUDA_VISIBLE_DEVICES=""
[36m(train_cnn_ray_tune pid=2076638)[0m 2025-10-28 13:48:17.982665: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:170] CUDA_VISIBLE_DEVICES is set to an empty string - this hides all GPUs from CUDA
[36m(train_cnn_ray_tune pid=2076638)[0m 2025-10-28 13:48:17.982671: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:178] verbose logging is disabled. Rerun with verbose logging (usually --v=1 or --vmodule=cuda_diagnostics=1) to get more diagnostic output from this module
[36m(train_cnn_ray_tune pid=2076638)[0m 2025-10-28 13:48:17.982677: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:183] retrieving CUDA diagnostic information for host: simur-MS-7B94
[36m(train_cnn_ray_tune pid=2076638)[0m 2025-10-28 13:48:17.982681: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:190] hostname: simur-MS-7B94
[36m(train_cnn_ray_tune pid=2076638)[0m 2025-10-28 13:48:17.982898: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:197] libcuda reported version is: 570.133.7
[36m(train_cnn_ray_tune pid=2076638)[0m 2025-10-28 13:48:17.982938: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:201] kernel reported version is: 570.133.7
[36m(train_cnn_ray_tune pid=2076638)[0m 2025-10-28 13:48:17.982944: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:291] kernel version seems to match DSO: 570.133.7
╭──────────────────────────────────────╮
│ Trial trial_5c837 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            72 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00034 │
╰──────────────────────────────────────╯
Trial trial_5c837 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_5c837 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                           125 │
│ funcion_activacion              relu │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00284 │
╰──────────────────────────────────────╯
Trial trial_5c837 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_5c837 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                           147 │
│ funcion_activacion              tanh │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00007 │
╰──────────────────────────────────────╯
Trial trial_5c837 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_5c837 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                           102 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00013 │
╰──────────────────────────────────────╯
Trial trial_5c837 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_5c837 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                            80 │
│ funcion_activacion              relu │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_5c837 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_5c837 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            97 │
│ funcion_activacion              relu │
│ numero_filtros                   128 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00124 │
╰──────────────────────────────────────╯
Trial trial_5c837 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_5c837 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            64 │
│ funcion_activacion              relu │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00016 │
╰──────────────────────────────────────╯
Trial trial_5c837 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_5c837 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                           116 │
│ funcion_activacion              tanh │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00128 │
╰──────────────────────────────────────╯
Trial trial_5c837 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_5c837 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            91 │
│ funcion_activacion              tanh │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00327 │
╰──────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2076638)[0m Model: "sequential"
[36m(train_cnn_ray_tune pid=2076638)[0m ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
[36m(train_cnn_ray_tune pid=2076638)[0m ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
[36m(train_cnn_ray_tune pid=2076638)[0m ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
[36m(train_cnn_ray_tune pid=2076638)[0m │ conv1d (Conv1D)                 │ (None, 6, 32)          │        40,032 │
[36m(train_cnn_ray_tune pid=2076638)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=2076638)[0m │ layer_normalization             │ (None, 6, 32)          │            64 │
[36m(train_cnn_ray_tune pid=2076638)[0m │ (LayerNormalization)            │                        │               │
[36m(train_cnn_ray_tune pid=2076638)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=2076638)[0m │ dropout (Dropout)               │ (None, 6, 32)          │             0 │
[36m(train_cnn_ray_tune pid=2076638)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=2076638)[0m │ conv1d_1 (Conv1D)               │ (None, 6, 32)          │         5,152 │
[36m(train_cnn_ray_tune pid=2076638)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=2076638)[0m │ layer_normalization_1           │ (None, 6, 32)          │            64 │
[36m(train_cnn_ray_tune pid=2076638)[0m │ (LayerNormalization)            │                        │               │
[36m(train_cnn_ray_tune pid=2076638)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=2076638)[0m │ dropout_1 (Dropout)             │ (None, 6, 32)          │             0 │
[36m(train_cnn_ray_tune pid=2076638)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=2076638)[0m │ conv1d_2 (Conv1D)               │ (None, 6, 32)          │         5,152 │
[36m(train_cnn_ray_tune pid=2076638)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=2076638)[0m │ layer_normalization_2           │ (None, 6, 32)          │            64 │
[36m(train_cnn_ray_tune pid=2076638)[0m │ (LayerNormalization)            │                        │               │
[36m(train_cnn_ray_tune pid=2076638)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=2076638)[0m │ dropout_2 (Dropout)             │ (None, 6, 32)          │             0 │
[36m(train_cnn_ray_tune pid=2076638)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=2076638)[0m │ global_average_pooling1d        │ (None, 32)             │             0 │
[36m(train_cnn_ray_tune pid=2076638)[0m │ (GlobalAveragePooling1D)        │                        │               │
[36m(train_cnn_ray_tune pid=2076638)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=2076638)[0m │ dropout_3 (Dropout)             │ (None, 32)             │             0 │
[36m(train_cnn_ray_tune pid=2076638)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=2076638)[0m │ dense (Dense)                   │ (None, 15)             │           495 │
[36m(train_cnn_ray_tune pid=2076638)[0m └─────────────────────────────────┴────────────────────────┴───────────────┘
[36m(train_cnn_ray_tune pid=2076638)[0m  Total params: 51,023 (199.31 KB)
[36m(train_cnn_ray_tune pid=2076638)[0m  Trainable params: 51,023 (199.31 KB)
[36m(train_cnn_ray_tune pid=2076638)[0m  Non-trainable params: 0 (0.00 B)
[36m(train_cnn_ray_tune pid=2076638)[0m Epoch 1/128
[36m(train_cnn_ray_tune pid=2076666)[0m 
[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10:14[0m 2s/step - accuracy: 0.0938 - loss: 3.5222
[36m(train_cnn_ray_tune pid=2076665)[0m 
[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10:15[0m 2s/step - accuracy: 0.0625 - loss: 3.8908
[1m  4/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.0687 - loss: 3.7896 
[36m(train_cnn_ray_tune pid=2076666)[0m 
[1m  3/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 28ms/step - accuracy: 0.0894 - loss: 3.5630 
[1m  6/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.0905 - loss: 3.5777
[36m(train_cnn_ray_tune pid=2076665)[0m 
[1m  7/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.0773 - loss: 3.7248
[36m(train_cnn_ray_tune pid=2076665)[0m 
[1m 10/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.0841 - loss: 3.6886
[1m 13/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.0872 - loss: 3.6627
[36m(train_cnn_ray_tune pid=2076669)[0m 
[1m  4/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 24ms/step - accuracy: 0.0775 - loss: 3.7292 
[36m(train_cnn_ray_tune pid=2076670)[0m 
[1m  4/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 18ms/step - accuracy: 0.0599 - loss: 3.8711
[1m  6/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 21ms/step - accuracy: 0.0682 - loss: 3.7822
[36m(train_cnn_ray_tune pid=2076670)[0m 
[1m  9/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.0732 - loss: 3.6946
[1m 12/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.0764 - loss: 3.6495
[36m(train_cnn_ray_tune pid=2076670)[0m Model: "sequential"[32m [repeated 19x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-deduplication for more options.)[0m
[36m(train_cnn_ray_tune pid=2076670)[0m ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m │ global_average_pooling1d        │ (None, 64)             │             0 │[32m [repeated 112x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤[32m [repeated 206x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m │ layer_normalization             │ (None, 6, 64)          │           128 │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m │ (LayerNormalization)            │                        │               │[32m [repeated 56x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m │ dropout (Dropout)               │ (None, 6, 64)          │             0 │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m │ dropout_2 (Dropout)             │ (None, 64)             │             0 │[32m [repeated 56x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m │ (GlobalAveragePooling1D)        │                        │               │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m │ dense (Dense)                   │ (None, 15)             │           975 │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m └─────────────────────────────────┴────────────────────────┴───────────────┘[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m  Total params: 101,839 (397.81 KB)[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m  Trainable params: 101,839 (397.81 KB)[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m  Non-trainable params: 0 (0.00 B)[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m Epoch 1/91[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2076657)[0m 
[1m 62/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m6s[0m 27ms/step - accuracy: 0.0700 - loss: 3.5456
[1m 64/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m6s[0m 27ms/step - accuracy: 0.0702 - loss: 3.5444
[1m 66/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m6s[0m 27ms/step - accuracy: 0.0703 - loss: 3.5433
[36m(train_cnn_ray_tune pid=2076657)[0m 
[1m 91/292[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m5s[0m 28ms/step - accuracy: 0.0717 - loss: 3.5260
[1m 93/292[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m5s[0m 28ms/step - accuracy: 0.0719 - loss: 3.5246
[1m 95/292[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m5s[0m 28ms/step - accuracy: 0.0720 - loss: 3.5231
[36m(train_cnn_ray_tune pid=2076625)[0m 
[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m31:07[0m 6s/step - accuracy: 0.0625 - loss: 3.5387[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=2076631)[0m 
[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:03:49[0m 7s/step - accuracy: 0.0625 - loss: 3.6600
[1m  2/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m32s[0m 55ms/step - accuracy: 0.0469 - loss: 3.6376  [32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=2076668)[0m 
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 39ms/step - accuracy: 0.0813 - loss: 3.4500 
[1m 42/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 39ms/step - accuracy: 0.0807 - loss: 3.4518[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=2076656)[0m 
[1m 55/146[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m4s[0m 51ms/step - accuracy: 0.0671 - loss: 3.4423[32m [repeated 235x across cluster][0m
[36m(train_cnn_ray_tune pid=2076656)[0m 
[1m 57/146[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m4s[0m 51ms/step - accuracy: 0.0672 - loss: 3.4420
[1m 59/146[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m4s[0m 51ms/step - accuracy: 0.0672 - loss: 3.4416[32m [repeated 178x across cluster][0m
[36m(train_cnn_ray_tune pid=2076655)[0m 
[1m157/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m12s[0m 29ms/step - accuracy: 0.0757 - loss: 3.4194[32m [repeated 135x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
[1m167/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m11s[0m 28ms/step - accuracy: 0.0677 - loss: 3.3993
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m Epoch 2/127
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m Epoch 2/64
[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m Epoch 2/72[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m Epoch 2/102[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=2076668)[0m 
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Trial status: 20 RUNNING
Current time: 2025-10-28 13:48:41. Total running time: 30s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_5c837    RUNNING            2   adam            relu                                  128                 32                  5          0.000203836        127 │
│ trial_5c837    RUNNING            3   adam            relu                                   32                 32                  5          0.00208772         128 │
│ trial_5c837    RUNNING            3   rmsprop         tanh                                   32                 64                  3          0.00128416         116 │
│ trial_5c837    RUNNING            3   rmsprop         relu                                  128                 32                  3          0.000155082         64 │
│ trial_5c837    RUNNING            2   rmsprop         tanh                                   64                128                  3          0.000337639         72 │
│ trial_5c837    RUNNING            2   adam            tanh                                   64                 64                  3          0.0016925          119 │
│ trial_5c837    RUNNING            2   adam            tanh                                   64                 64                  3          0.000425983         69 │
│ trial_5c837    RUNNING            4   adam            tanh                                  128                 32                  3          3.88891e-05        139 │
│ trial_5c837    RUNNING            2   adam            relu                                   64                 64                  5          0.000136214        110 │
│ trial_5c837    RUNNING            3   rmsprop         tanh                                   64                 64                  3          7.35601e-05        147 │
│ trial_5c837    RUNNING            3   rmsprop         relu                                   32                 64                  5          0.00284314         125 │
│ trial_5c837    RUNNING            4   rmsprop         tanh                                   32                 32                  3          0.00284271         103 │
│ trial_5c837    RUNNING            3   rmsprop         relu                                  128                128                  3          0.00123868          97 │
│ trial_5c837    RUNNING            4   rmsprop         relu                                  128                 32                  5          1.39284e-05         80 │
│ trial_5c837    RUNNING            4   adam            relu                                   64                 32                  3          0.000166824         57 │
│ trial_5c837    RUNNING            2   adam            tanh                                   32                 64                  5          0.00326602          91 │
│ trial_5c837    RUNNING            4   adam            tanh                                   64                 32                  5          0.000133042        102 │
│ trial_5c837    RUNNING            2   rmsprop         tanh                                   64                128                  3          0.00419881         110 │
│ trial_5c837    RUNNING            4   adam            tanh                                   32                 32                  3          0.00235056         142 │
│ trial_5c837    RUNNING            3   rmsprop         tanh                                   32                 32                  3          5.54287e-05        123 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m38s[0m 65ms/step - accuracy: 0.0625 - loss: 2.7214
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m Epoch 5/127[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m Epoch 7/127[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m Epoch 8/127[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m Epoch 4/97[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m Epoch 4/147[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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Trial status: 20 RUNNING
Current time: 2025-10-28 13:49:11. Total running time: 1min 0s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_5c837    RUNNING            2   adam            relu                                  128                 32                  5          0.000203836        127 │
│ trial_5c837    RUNNING            3   adam            relu                                   32                 32                  5          0.00208772         128 │
│ trial_5c837    RUNNING            3   rmsprop         tanh                                   32                 64                  3          0.00128416         116 │
│ trial_5c837    RUNNING            3   rmsprop         relu                                  128                 32                  3          0.000155082         64 │
│ trial_5c837    RUNNING            2   rmsprop         tanh                                   64                128                  3          0.000337639         72 │
│ trial_5c837    RUNNING            2   adam            tanh                                   64                 64                  3          0.0016925          119 │
│ trial_5c837    RUNNING            2   adam            tanh                                   64                 64                  3          0.000425983         69 │
│ trial_5c837    RUNNING            4   adam            tanh                                  128                 32                  3          3.88891e-05        139 │
│ trial_5c837    RUNNING            2   adam            relu                                   64                 64                  5          0.000136214        110 │
│ trial_5c837    RUNNING            3   rmsprop         tanh                                   64                 64                  3          7.35601e-05        147 │
│ trial_5c837    RUNNING            3   rmsprop         relu                                   32                 64                  5          0.00284314         125 │
│ trial_5c837    RUNNING            4   rmsprop         tanh                                   32                 32                  3          0.00284271         103 │
│ trial_5c837    RUNNING            3   rmsprop         relu                                  128                128                  3          0.00123868          97 │
│ trial_5c837    RUNNING            4   rmsprop         relu                                  128                 32                  5          1.39284e-05         80 │
│ trial_5c837    RUNNING            4   adam            relu                                   64                 32                  3          0.000166824         57 │
│ trial_5c837    RUNNING            2   adam            tanh                                   32                 64                  5          0.00326602          91 │
│ trial_5c837    RUNNING            4   adam            tanh                                   64                 32                  5          0.000133042        102 │
│ trial_5c837    RUNNING            2   rmsprop         tanh                                   64                128                  3          0.00419881         110 │
│ trial_5c837    RUNNING            4   adam            tanh                                   32                 32                  3          0.00235056         142 │
│ trial_5c837    RUNNING            3   rmsprop         tanh                                   32                 32                  3          5.54287e-05        123 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m Epoch 5/57[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m Epoch 8/139[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m Epoch 7/110[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m Epoch 6/102[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m Epoch 5/116[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=2076657)[0m 
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Trial status: 20 RUNNING
Current time: 2025-10-28 13:49:41. Total running time: 1min 30s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_5c837    RUNNING            2   adam            relu                                  128                 32                  5          0.000203836        127 │
│ trial_5c837    RUNNING            3   adam            relu                                   32                 32                  5          0.00208772         128 │
│ trial_5c837    RUNNING            3   rmsprop         tanh                                   32                 64                  3          0.00128416         116 │
│ trial_5c837    RUNNING            3   rmsprop         relu                                  128                 32                  3          0.000155082         64 │
│ trial_5c837    RUNNING            2   rmsprop         tanh                                   64                128                  3          0.000337639         72 │
│ trial_5c837    RUNNING            2   adam            tanh                                   64                 64                  3          0.0016925          119 │
│ trial_5c837    RUNNING            2   adam            tanh                                   64                 64                  3          0.000425983         69 │
│ trial_5c837    RUNNING            4   adam            tanh                                  128                 32                  3          3.88891e-05        139 │
│ trial_5c837    RUNNING            2   adam            relu                                   64                 64                  5          0.000136214        110 │
│ trial_5c837    RUNNING            3   rmsprop         tanh                                   64                 64                  3          7.35601e-05        147 │
│ trial_5c837    RUNNING            3   rmsprop         relu                                   32                 64                  5          0.00284314         125 │
│ trial_5c837    RUNNING            4   rmsprop         tanh                                   32                 32                  3          0.00284271         103 │
│ trial_5c837    RUNNING            3   rmsprop         relu                                  128                128                  3          0.00123868          97 │
│ trial_5c837    RUNNING            4   rmsprop         relu                                  128                 32                  5          1.39284e-05         80 │
│ trial_5c837    RUNNING            4   adam            relu                                   64                 32                  3          0.000166824         57 │
│ trial_5c837    RUNNING            2   adam            tanh                                   32                 64                  5          0.00326602          91 │
│ trial_5c837    RUNNING            4   adam            tanh                                   64                 32                  5          0.000133042        102 │
│ trial_5c837    RUNNING            2   rmsprop         tanh                                   64                128                  3          0.00419881         110 │
│ trial_5c837    RUNNING            4   adam            tanh                                   32                 32                  3          0.00235056         142 │
│ trial_5c837    RUNNING            3   rmsprop         tanh                                   32                 32                  3          5.54287e-05        123 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m Epoch 9/110[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m Epoch 10/69[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m Epoch 8/110[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m Epoch 8/97[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m Epoch 9/57[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m Epoch 7/123[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=2076619)[0m 
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Trial status: 20 RUNNING
Current time: 2025-10-28 13:50:12. Total running time: 2min 0s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_5c837    RUNNING            2   adam            relu                                  128                 32                  5          0.000203836        127 │
│ trial_5c837    RUNNING            3   adam            relu                                   32                 32                  5          0.00208772         128 │
│ trial_5c837    RUNNING            3   rmsprop         tanh                                   32                 64                  3          0.00128416         116 │
│ trial_5c837    RUNNING            3   rmsprop         relu                                  128                 32                  3          0.000155082         64 │
│ trial_5c837    RUNNING            2   rmsprop         tanh                                   64                128                  3          0.000337639         72 │
│ trial_5c837    RUNNING            2   adam            tanh                                   64                 64                  3          0.0016925          119 │
│ trial_5c837    RUNNING            2   adam            tanh                                   64                 64                  3          0.000425983         69 │
│ trial_5c837    RUNNING            4   adam            tanh                                  128                 32                  3          3.88891e-05        139 │
│ trial_5c837    RUNNING            2   adam            relu                                   64                 64                  5          0.000136214        110 │
│ trial_5c837    RUNNING            3   rmsprop         tanh                                   64                 64                  3          7.35601e-05        147 │
│ trial_5c837    RUNNING            3   rmsprop         relu                                   32                 64                  5          0.00284314         125 │
│ trial_5c837    RUNNING            4   rmsprop         tanh                                   32                 32                  3          0.00284271         103 │
│ trial_5c837    RUNNING            3   rmsprop         relu                                  128                128                  3          0.00123868          97 │
│ trial_5c837    RUNNING            4   rmsprop         relu                                  128                 32                  5          1.39284e-05         80 │
│ trial_5c837    RUNNING            4   adam            relu                                   64                 32                  3          0.000166824         57 │
│ trial_5c837    RUNNING            2   adam            tanh                                   32                 64                  5          0.00326602          91 │
│ trial_5c837    RUNNING            4   adam            tanh                                   64                 32                  5          0.000133042        102 │
│ trial_5c837    RUNNING            2   rmsprop         tanh                                   64                128                  3          0.00419881         110 │
│ trial_5c837    RUNNING            4   adam            tanh                                   32                 32                  3          0.00235056         142 │
│ trial_5c837    RUNNING            3   rmsprop         tanh                                   32                 32                  3          5.54287e-05        123 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m Epoch 15/139[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m Epoch 10/110[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m Epoch 10/72[32m [repeated 10x across cluster][0m
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m Epoch 21/64[32m [repeated 6x across cluster][0m
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m Epoch 27/127[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2076631)[0m 
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Trial status: 20 RUNNING
Current time: 2025-10-28 13:50:42. Total running time: 2min 30s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_5c837    RUNNING            2   adam            relu                                  128                 32                  5          0.000203836        127 │
│ trial_5c837    RUNNING            3   adam            relu                                   32                 32                  5          0.00208772         128 │
│ trial_5c837    RUNNING            3   rmsprop         tanh                                   32                 64                  3          0.00128416         116 │
│ trial_5c837    RUNNING            3   rmsprop         relu                                  128                 32                  3          0.000155082         64 │
│ trial_5c837    RUNNING            2   rmsprop         tanh                                   64                128                  3          0.000337639         72 │
│ trial_5c837    RUNNING            2   adam            tanh                                   64                 64                  3          0.0016925          119 │
│ trial_5c837    RUNNING            2   adam            tanh                                   64                 64                  3          0.000425983         69 │
│ trial_5c837    RUNNING            4   adam            tanh                                  128                 32                  3          3.88891e-05        139 │
│ trial_5c837    RUNNING            2   adam            relu                                   64                 64                  5          0.000136214        110 │
│ trial_5c837    RUNNING            3   rmsprop         tanh                                   64                 64                  3          7.35601e-05        147 │
│ trial_5c837    RUNNING            3   rmsprop         relu                                   32                 64                  5          0.00284314         125 │
│ trial_5c837    RUNNING            4   rmsprop         tanh                                   32                 32                  3          0.00284271         103 │
│ trial_5c837    RUNNING            3   rmsprop         relu                                  128                128                  3          0.00123868          97 │
│ trial_5c837    RUNNING            4   rmsprop         relu                                  128                 32                  5          1.39284e-05         80 │
│ trial_5c837    RUNNING            4   adam            relu                                   64                 32                  3          0.000166824         57 │
│ trial_5c837    RUNNING            2   adam            tanh                                   32                 64                  5          0.00326602          91 │
│ trial_5c837    RUNNING            4   adam            tanh                                   64                 32                  5          0.000133042        102 │
│ trial_5c837    RUNNING            2   rmsprop         tanh                                   64                128                  3          0.00419881         110 │
│ trial_5c837    RUNNING            4   adam            tanh                                   32                 32                  3          0.00235056         142 │
│ trial_5c837    RUNNING            3   rmsprop         tanh                                   32                 32                  3          5.54287e-05        123 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m Epoch 28/127[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m Epoch 12/72[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m Epoch 10/91[32m [repeated 10x across cluster][0m
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m Epoch 13/102[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m Epoch 9/103[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=2076645)[0m 
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Trial status: 20 RUNNING
Current time: 2025-10-28 13:51:12. Total running time: 3min 0s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_5c837    RUNNING            2   adam            relu                                  128                 32                  5          0.000203836        127 │
│ trial_5c837    RUNNING            3   adam            relu                                   32                 32                  5          0.00208772         128 │
│ trial_5c837    RUNNING            3   rmsprop         tanh                                   32                 64                  3          0.00128416         116 │
│ trial_5c837    RUNNING            3   rmsprop         relu                                  128                 32                  3          0.000155082         64 │
│ trial_5c837    RUNNING            2   rmsprop         tanh                                   64                128                  3          0.000337639         72 │
│ trial_5c837    RUNNING            2   adam            tanh                                   64                 64                  3          0.0016925          119 │
│ trial_5c837    RUNNING            2   adam            tanh                                   64                 64                  3          0.000425983         69 │
│ trial_5c837    RUNNING            4   adam            tanh                                  128                 32                  3          3.88891e-05        139 │
│ trial_5c837    RUNNING            2   adam            relu                                   64                 64                  5          0.000136214        110 │
│ trial_5c837    RUNNING            3   rmsprop         tanh                                   64                 64                  3          7.35601e-05        147 │
│ trial_5c837    RUNNING            3   rmsprop         relu                                   32                 64                  5          0.00284314         125 │
│ trial_5c837    RUNNING            4   rmsprop         tanh                                   32                 32                  3          0.00284271         103 │
│ trial_5c837    RUNNING            3   rmsprop         relu                                  128                128                  3          0.00123868          97 │
│ trial_5c837    RUNNING            4   rmsprop         relu                                  128                 32                  5          1.39284e-05         80 │
│ trial_5c837    RUNNING            4   adam            relu                                   64                 32                  3          0.000166824         57 │
│ trial_5c837    RUNNING            2   adam            tanh                                   32                 64                  5          0.00326602          91 │
│ trial_5c837    RUNNING            4   adam            tanh                                   64                 32                  5          0.000133042        102 │
│ trial_5c837    RUNNING            2   rmsprop         tanh                                   64                128                  3          0.00419881         110 │
│ trial_5c837    RUNNING            4   adam            tanh                                   32                 32                  3          0.00235056         142 │
│ trial_5c837    RUNNING            3   rmsprop         tanh                                   32                 32                  3          5.54287e-05        123 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2076659)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m Epoch 23/80[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m Epoch 15/57[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m Epoch 9/142[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m Epoch 12/91[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m Epoch 20/110[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m Epoch 31/64[32m [repeated 7x across cluster][0m
Trial status: 20 RUNNING
Current time: 2025-10-28 13:51:42. Total running time: 3min 30s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_5c837    RUNNING            2   adam            relu                                  128                 32                  5          0.000203836        127 │
│ trial_5c837    RUNNING            3   adam            relu                                   32                 32                  5          0.00208772         128 │
│ trial_5c837    RUNNING            3   rmsprop         tanh                                   32                 64                  3          0.00128416         116 │
│ trial_5c837    RUNNING            3   rmsprop         relu                                  128                 32                  3          0.000155082         64 │
│ trial_5c837    RUNNING            2   rmsprop         tanh                                   64                128                  3          0.000337639         72 │
│ trial_5c837    RUNNING            2   adam            tanh                                   64                 64                  3          0.0016925          119 │
│ trial_5c837    RUNNING            2   adam            tanh                                   64                 64                  3          0.000425983         69 │
│ trial_5c837    RUNNING            4   adam            tanh                                  128                 32                  3          3.88891e-05        139 │
│ trial_5c837    RUNNING            2   adam            relu                                   64                 64                  5          0.000136214        110 │
│ trial_5c837    RUNNING            3   rmsprop         tanh                                   64                 64                  3          7.35601e-05        147 │
│ trial_5c837    RUNNING            3   rmsprop         relu                                   32                 64                  5          0.00284314         125 │
│ trial_5c837    RUNNING            4   rmsprop         tanh                                   32                 32                  3          0.00284271         103 │
│ trial_5c837    RUNNING            3   rmsprop         relu                                  128                128                  3          0.00123868          97 │
│ trial_5c837    RUNNING            4   rmsprop         relu                                  128                 32                  5          1.39284e-05         80 │
│ trial_5c837    RUNNING            4   adam            relu                                   64                 32                  3          0.000166824         57 │
│ trial_5c837    RUNNING            2   adam            tanh                                   32                 64                  5          0.00326602          91 │
│ trial_5c837    RUNNING            4   adam            tanh                                   64                 32                  5          0.000133042        102 │
│ trial_5c837    RUNNING            2   rmsprop         tanh                                   64                128                  3          0.00419881         110 │
│ trial_5c837    RUNNING            4   adam            tanh                                   32                 32                  3          0.00235056         142 │
│ trial_5c837    RUNNING            3   rmsprop         tanh                                   32                 32                  3          5.54287e-05        123 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m Epoch 21/110[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m Epoch 17/147[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m Epoch 28/80[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m Epoch 23/119[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m Epoch 35/64[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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Trial status: 20 RUNNING
Current time: 2025-10-28 13:52:12. Total running time: 4min 0s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_5c837    RUNNING            2   adam            relu                                  128                 32                  5          0.000203836        127 │
│ trial_5c837    RUNNING            3   adam            relu                                   32                 32                  5          0.00208772         128 │
│ trial_5c837    RUNNING            3   rmsprop         tanh                                   32                 64                  3          0.00128416         116 │
│ trial_5c837    RUNNING            3   rmsprop         relu                                  128                 32                  3          0.000155082         64 │
│ trial_5c837    RUNNING            2   rmsprop         tanh                                   64                128                  3          0.000337639         72 │
│ trial_5c837    RUNNING            2   adam            tanh                                   64                 64                  3          0.0016925          119 │
│ trial_5c837    RUNNING            2   adam            tanh                                   64                 64                  3          0.000425983         69 │
│ trial_5c837    RUNNING            4   adam            tanh                                  128                 32                  3          3.88891e-05        139 │
│ trial_5c837    RUNNING            2   adam            relu                                   64                 64                  5          0.000136214        110 │
│ trial_5c837    RUNNING            3   rmsprop         tanh                                   64                 64                  3          7.35601e-05        147 │
│ trial_5c837    RUNNING            3   rmsprop         relu                                   32                 64                  5          0.00284314         125 │
│ trial_5c837    RUNNING            4   rmsprop         tanh                                   32                 32                  3          0.00284271         103 │
│ trial_5c837    RUNNING            3   rmsprop         relu                                  128                128                  3          0.00123868          97 │
│ trial_5c837    RUNNING            4   rmsprop         relu                                  128                 32                  5          1.39284e-05         80 │
│ trial_5c837    RUNNING            4   adam            relu                                   64                 32                  3          0.000166824         57 │
│ trial_5c837    RUNNING            2   adam            tanh                                   32                 64                  5          0.00326602          91 │
│ trial_5c837    RUNNING            4   adam            tanh                                   64                 32                  5          0.000133042        102 │
│ trial_5c837    RUNNING            2   rmsprop         tanh                                   64                128                  3          0.00419881         110 │
│ trial_5c837    RUNNING            4   adam            tanh                                   32                 32                  3          0.00235056         142 │
│ trial_5c837    RUNNING            3   rmsprop         tanh                                   32                 32                  3          5.54287e-05        123 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2076659)[0m Epoch 24/119[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2076659)[0m 
[1m  2/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 51ms/step - accuracy: 0.3203 - loss: 1.9872 [32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
[1m421/584[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m4s[0m 27ms/step - accuracy: 0.0955 - loss: 2.8087
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[1m  4/146[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 45ms/step - accuracy: 0.1955 - loss: 2.3159
[1m  5/146[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 46ms/step - accuracy: 0.1933 - loss: 2.3130
[36m(train_cnn_ray_tune pid=2076613)[0m 
[1m 73/146[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m2s[0m 31ms/step - accuracy: 0.2312 - loss: 2.0995[32m [repeated 335x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
[1m114/584[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m15s[0m 33ms/step - accuracy: 0.1688 - loss: 2.3737[32m [repeated 46x across cluster][0m
[36m(train_cnn_ray_tune pid=2076631)[0m 
[1m128/584[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m15s[0m 33ms/step - accuracy: 0.1688 - loss: 2.3754
[1m130/584[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m15s[0m 33ms/step - accuracy: 0.1688 - loss: 2.3757[32m [repeated 63x across cluster][0m
[36m(train_cnn_ray_tune pid=2076645)[0m 
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m21s[0m 36ms/step - accuracy: 0.1603 - loss: 2.4260 - val_accuracy: 0.1763 - val_loss: 2.3537[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2076656)[0m 
[1m  1/146[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 151ms/step - accuracy: 0.0625 - loss: 3.1328[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=2076645)[0m 
[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:08[0m 118ms/step - accuracy: 0.1250 - loss: 2.5146
[1m  3/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 33ms/step - accuracy: 0.1632 - loss: 2.4611  [32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=2076638)[0m 
[1m171/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m11s[0m 28ms/step - accuracy: 0.2167 - loss: 2.1236
[1m173/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m11s[0m 28ms/step - accuracy: 0.2168 - loss: 2.1235
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[36m(train_cnn_ray_tune pid=2076665)[0m 
[1m 22/292[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 37ms/step - accuracy: 0.2329 - loss: 2.1856 
[1m 24/292[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 37ms/step - accuracy: 0.2334 - loss: 2.1842[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=2076613)[0m Epoch 45/127[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=2076613)[0m 
[1m  3/146[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 33ms/step - accuracy: 0.2001 - loss: 2.0788  [32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=2076669)[0m 
[1m 23/292[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 32ms/step - accuracy: 0.2477 - loss: 2.1119[32m [repeated 379x across cluster][0m
[36m(train_cnn_ray_tune pid=2076613)[0m 
[1m 52/146[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m3s[0m 33ms/step - accuracy: 0.2200 - loss: 2.0996
[1m 54/146[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m3s[0m 33ms/step - accuracy: 0.2200 - loss: 2.1000[32m [repeated 339x across cluster][0m
[36m(train_cnn_ray_tune pid=2076631)[0m 
[1m255/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m10s[0m 33ms/step - accuracy: 0.1669 - loss: 2.3847[32m [repeated 55x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m Epoch 15/91[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=2076659)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m Epoch 38/64[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m Epoch 32/139[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m Epoch 33/80[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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Trial status: 20 RUNNING
Current time: 2025-10-28 13:52:42. Total running time: 4min 30s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_5c837    RUNNING            2   adam            relu                                  128                 32                  5          0.000203836        127 │
│ trial_5c837    RUNNING            3   adam            relu                                   32                 32                  5          0.00208772         128 │
│ trial_5c837    RUNNING            3   rmsprop         tanh                                   32                 64                  3          0.00128416         116 │
│ trial_5c837    RUNNING            3   rmsprop         relu                                  128                 32                  3          0.000155082         64 │
│ trial_5c837    RUNNING            2   rmsprop         tanh                                   64                128                  3          0.000337639         72 │
│ trial_5c837    RUNNING            2   adam            tanh                                   64                 64                  3          0.0016925          119 │
│ trial_5c837    RUNNING            2   adam            tanh                                   64                 64                  3          0.000425983         69 │
│ trial_5c837    RUNNING            4   adam            tanh                                  128                 32                  3          3.88891e-05        139 │
│ trial_5c837    RUNNING            2   adam            relu                                   64                 64                  5          0.000136214        110 │
│ trial_5c837    RUNNING            3   rmsprop         tanh                                   64                 64                  3          7.35601e-05        147 │
│ trial_5c837    RUNNING            3   rmsprop         relu                                   32                 64                  5          0.00284314         125 │
│ trial_5c837    RUNNING            4   rmsprop         tanh                                   32                 32                  3          0.00284271         103 │
│ trial_5c837    RUNNING            3   rmsprop         relu                                  128                128                  3          0.00123868          97 │
│ trial_5c837    RUNNING            4   rmsprop         relu                                  128                 32                  5          1.39284e-05         80 │
│ trial_5c837    RUNNING            4   adam            relu                                   64                 32                  3          0.000166824         57 │
│ trial_5c837    RUNNING            2   adam            tanh                                   32                 64                  5          0.00326602          91 │
│ trial_5c837    RUNNING            4   adam            tanh                                   64                 32                  5          0.000133042        102 │
│ trial_5c837    RUNNING            2   rmsprop         tanh                                   64                128                  3          0.00419881         110 │
│ trial_5c837    RUNNING            4   adam            tanh                                   32                 32                  3          0.00235056         142 │
│ trial_5c837    RUNNING            3   rmsprop         tanh                                   32                 32                  3          5.54287e-05        123 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m Epoch 27/119[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m Epoch 21/72[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m Epoch 28/119[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m Epoch 14/103[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m Epoch 29/119[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m Epoch 23/57[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
[1m63/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 10ms/step
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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Trial status: 20 RUNNING
Current time: 2025-10-28 13:53:12. Total running time: 5min 1s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_5c837    RUNNING            2   adam            relu                                  128                 32                  5          0.000203836        127 │
│ trial_5c837    RUNNING            3   adam            relu                                   32                 32                  5          0.00208772         128 │
│ trial_5c837    RUNNING            3   rmsprop         tanh                                   32                 64                  3          0.00128416         116 │
│ trial_5c837    RUNNING            3   rmsprop         relu                                  128                 32                  3          0.000155082         64 │
│ trial_5c837    RUNNING            2   rmsprop         tanh                                   64                128                  3          0.000337639         72 │
│ trial_5c837    RUNNING            2   adam            tanh                                   64                 64                  3          0.0016925          119 │
│ trial_5c837    RUNNING            2   adam            tanh                                   64                 64                  3          0.000425983         69 │
│ trial_5c837    RUNNING            4   adam            tanh                                  128                 32                  3          3.88891e-05        139 │
│ trial_5c837    RUNNING            2   adam            relu                                   64                 64                  5          0.000136214        110 │
│ trial_5c837    RUNNING            3   rmsprop         tanh                                   64                 64                  3          7.35601e-05        147 │
│ trial_5c837    RUNNING            3   rmsprop         relu                                   32                 64                  5          0.00284314         125 │
│ trial_5c837    RUNNING            4   rmsprop         tanh                                   32                 32                  3          0.00284271         103 │
│ trial_5c837    RUNNING            3   rmsprop         relu                                  128                128                  3          0.00123868          97 │
│ trial_5c837    RUNNING            4   rmsprop         relu                                  128                 32                  5          1.39284e-05         80 │
│ trial_5c837    RUNNING            4   adam            relu                                   64                 32                  3          0.000166824         57 │
│ trial_5c837    RUNNING            2   adam            tanh                                   32                 64                  5          0.00326602          91 │
│ trial_5c837    RUNNING            4   adam            tanh                                   64                 32                  5          0.000133042        102 │
│ trial_5c837    RUNNING            2   rmsprop         tanh                                   64                128                  3          0.00419881         110 │
│ trial_5c837    RUNNING            4   adam            tanh                                   32                 32                  3          0.00235056         142 │
│ trial_5c837    RUNNING            3   rmsprop         tanh                                   32                 32                  3          5.54287e-05        123 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m Epoch 30/119[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=2076613)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454
[36m(train_cnn_ray_tune pid=2076613)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2076670)[0m 2025-10-28 13:48:15.244933: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m 2025-10-28 13:48:15.265975: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m WARNING: All log messages before absl::InitializeLog() is called are written to STDERR[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m E0000 00:00:1761655695.293731 2077920 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m E0000 00:00:1761655695.301943 2077920 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m W0000 00:00:1761655695.322076 2077920 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.[32m [repeated 76x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m 2025-10-28 13:48:15.328380: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m   warnings.warn([32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m 2025-10-28 13:48:18.576623: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m 2025-10-28 13:48:18.576772: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:167] env: CUDA_VISIBLE_DEVICES=""[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m 2025-10-28 13:48:18.576784: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:170] CUDA_VISIBLE_DEVICES is set to an empty string - this hides all GPUs from CUDA[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m 2025-10-28 13:48:18.576790: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:178] verbose logging is disabled. Rerun with verbose logging (usually --v=1 or --vmodule=cuda_diagnostics=1) to get more diagnostic output from this module[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m 2025-10-28 13:48:18.576796: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:183] retrieving CUDA diagnostic information for host: simur-MS-7B94[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m 2025-10-28 13:48:18.576801: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:190] hostname: simur-MS-7B94[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m 2025-10-28 13:48:18.577224: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:197] libcuda reported version is: 570.133.7[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m 2025-10-28 13:48:18.577305: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:201] kernel reported version is: 570.133.7[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2076670)[0m 2025-10-28 13:48:18.577311: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:291] kernel version seems to match DSO: 570.133.7[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2076613)[0m 
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[36m(train_cnn_ray_tune pid=2076613)[0m 
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Trial trial_5c837 finished iteration 1 at 2025-10-28 13:53:13. Total running time: 5min 1s
╭──────────────────────────────────────╮
│ Trial trial_5c837 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             298.758 │
│ time_total_s                 298.758 │
│ training_iteration                 1 │
│ val_accuracy                 0.27641 │
╰──────────────────────────────────────╯

Trial trial_5c837 completed after 1 iterations at 2025-10-28 13:53:13. Total running time: 5min 1s
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m Epoch 24/102[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m Epoch 17/128[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m Epoch 40/139[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m Epoch 26/110[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m 
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Trial status: 1 TERMINATED | 19 RUNNING
Current time: 2025-10-28 13:53:42. Total running time: 5min 31s
Logical resource usage: 19.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_5c837    RUNNING              3   adam            relu                                   32                 32                  5          0.00208772         128                                              │
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   32                 64                  3          0.00128416         116                                              │
│ trial_5c837    RUNNING              3   rmsprop         relu                                  128                 32                  3          0.000155082         64                                              │
│ trial_5c837    RUNNING              2   rmsprop         tanh                                   64                128                  3          0.000337639         72                                              │
│ trial_5c837    RUNNING              2   adam            tanh                                   64                 64                  3          0.0016925          119                                              │
│ trial_5c837    RUNNING              2   adam            tanh                                   64                 64                  3          0.000425983         69                                              │
│ trial_5c837    RUNNING              4   adam            tanh                                  128                 32                  3          3.88891e-05        139                                              │
│ trial_5c837    RUNNING              2   adam            relu                                   64                 64                  5          0.000136214        110                                              │
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   64                 64                  3          7.35601e-05        147                                              │
│ trial_5c837    RUNNING              3   rmsprop         relu                                   32                 64                  5          0.00284314         125                                              │
│ trial_5c837    RUNNING              4   rmsprop         tanh                                   32                 32                  3          0.00284271         103                                              │
│ trial_5c837    RUNNING              3   rmsprop         relu                                  128                128                  3          0.00123868          97                                              │
│ trial_5c837    RUNNING              4   rmsprop         relu                                  128                 32                  5          1.39284e-05         80                                              │
│ trial_5c837    RUNNING              4   adam            relu                                   64                 32                  3          0.000166824         57                                              │
│ trial_5c837    RUNNING              2   adam            tanh                                   32                 64                  5          0.00326602          91                                              │
│ trial_5c837    RUNNING              4   adam            tanh                                   64                 32                  5          0.000133042        102                                              │
│ trial_5c837    RUNNING              2   rmsprop         tanh                                   64                128                  3          0.00419881         110                                              │
│ trial_5c837    RUNNING              4   adam            tanh                                   32                 32                  3          0.00235056         142                                              │
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   32                 32                  3          5.54287e-05        123                                              │
│ trial_5c837    TERMINATED           2   adam            relu                                  128                 32                  5          0.000203836        127        1            298.758         0.276411 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2076637)[0m 
[1m  2/146[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 76ms/step - accuracy: 0.3633 - loss: 1.6673 
[1m  3/146[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 72ms/step - accuracy: 0.3568 - loss: 1.6702
[36m(train_cnn_ray_tune pid=2076637)[0m Epoch 24/97[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m Epoch 35/69[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m Epoch 16/142[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
[1m 63/146[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m3s[0m 39ms/step - accuracy: 0.0729 - loss: 3.0730[32m [repeated 274x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
[1m  3/146[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 38ms/step - accuracy: 0.0972 - loss: 2.8851  
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
[1m 96/584[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m10s[0m 22ms/step - accuracy: 0.2426 - loss: 2.1119
[1m 98/584[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m10s[0m 22ms/step - accuracy: 0.2427 - loss: 2.1124[32m [repeated 85x across cluster][0m
[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m29s[0m 103ms/step - accuracy: 0.0312 - loss: 2.7551[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m Epoch 54/64[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=2076654)[0m 
[1m  3/146[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 31ms/step - accuracy: 0.2339 - loss: 2.1320  [32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m43s[0m 74ms/step - accuracy: 0.2500 - loss: 2.0255
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 83ms/step - accuracy: 0.3594 - loss: 1.8376
[1m  3/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 26ms/step - accuracy: 0.3168 - loss: 1.8424 
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
[1m  1/146[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 96ms/step - accuracy: 0.0547 - loss: 3.2959[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=2076637)[0m 
[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 256ms/step
[36m(train_cnn_ray_tune pid=2076637)[0m 
[1m 4/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 23ms/step  
[1m 8/86[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 18ms/step
[36m(train_cnn_ray_tune pid=2076637)[0m 
[1m12/86[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[36m(train_cnn_ray_tune pid=2076671)[0m 
[1m262/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m8s[0m 27ms/step - accuracy: 0.2868 - loss: 1.9394
[1m264/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m8s[0m 27ms/step - accuracy: 0.2868 - loss: 1.9393
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[36m(train_cnn_ray_tune pid=2076637)[0m 
[1m16/86[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[36m(train_cnn_ray_tune pid=2076637)[0m 
[1m21/86[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[36m(train_cnn_ray_tune pid=2076637)[0m 
[1m25/86[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[36m(train_cnn_ray_tune pid=2076637)[0m 
[1m29/86[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[36m(train_cnn_ray_tune pid=2076637)[0m 
[1m32/86[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
[1m41/86[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 16ms/step
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[36m(train_cnn_ray_tune pid=2076637)[0m 
[1m48/86[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 16ms/step
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[36m(train_cnn_ray_tune pid=2076637)[0m 
[1m56/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 16ms/step
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[36m(train_cnn_ray_tune pid=2076637)[0m 
[1m64/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 16ms/step
[36m(train_cnn_ray_tune pid=2076654)[0m Epoch 55/64[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=2076637)[0m 
[1m69/86[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 15ms/step
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[36m(train_cnn_ray_tune pid=2076637)[0m 
[1m77/86[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 15ms/step
[1m81/86[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 15ms/step
[36m(train_cnn_ray_tune pid=2076637)[0m 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[36m(train_cnn_ray_tune pid=2076637)[0m 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 18ms/step
[36m(train_cnn_ray_tune pid=2076637)[0m 
[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 45ms/step
[1m  5/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 15ms/step
[36m(train_cnn_ray_tune pid=2076637)[0m 
[1m  8/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 11/169[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[36m(train_cnn_ray_tune pid=2076637)[0m 
[1m 14/169[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 19/169[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 15ms/step
[36m(train_cnn_ray_tune pid=2076637)[0m 
[1m 23/169[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 15ms/step
[36m(train_cnn_ray_tune pid=2076637)[0m 
[1m 28/169[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 15ms/step
[36m(train_cnn_ray_tune pid=2076637)[0m 
[1m 33/169[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 15ms/step
[1m 36/169[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[36m(train_cnn_ray_tune pid=2076637)[0m 
[1m 41/169[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 14ms/step
[36m(train_cnn_ray_tune pid=2076657)[0m 
[1m  3/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 27ms/step - accuracy: 0.3559 - loss: 1.9539  
[36m(train_cnn_ray_tune pid=2076637)[0m 
[1m 46/169[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 14ms/step
[1m 50/169[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 14ms/step
[36m(train_cnn_ray_tune pid=2076637)[0m 
[1m 55/169[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 14ms/step
[1m 59/169[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 14ms/step
[36m(train_cnn_ray_tune pid=2076637)[0m 
[1m 63/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 14ms/step
[1m 66/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 14ms/step
[36m(train_cnn_ray_tune pid=2076637)[0m 
[1m 70/169[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 14ms/step
[36m(train_cnn_ray_tune pid=2076656)[0m 
[1m139/146[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 40ms/step - accuracy: 0.0723 - loss: 3.0819[32m [repeated 319x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
[1m269/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.1032 - loss: 2.7274
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454
[36m(train_cnn_ray_tune pid=2076637)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[36m(train_cnn_ray_tune pid=2076637)[0m 
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[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 14ms/step

Trial trial_5c837 finished iteration 1 at 2025-10-28 13:54:11. Total running time: 5min 59s
╭──────────────────────────────────────╮
│ Trial trial_5c837 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             356.744 │
│ time_total_s                 356.744 │
│ training_iteration                 1 │
│ val_accuracy                 0.35079 │
╰──────────────────────────────────────╯

Trial trial_5c837 completed after 1 iterations at 2025-10-28 13:54:11. Total running time: 5min 59s
[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m30s[0m 103ms/step - accuracy: 0.1406 - loss: 2.5716
[1m  3/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 26ms/step - accuracy: 0.1484 - loss: 2.5665  

Trial status: 2 TERMINATED | 18 RUNNING
Current time: 2025-10-28 13:54:12. Total running time: 6min 1s
Logical resource usage: 18.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_5c837    RUNNING              3   adam            relu                                   32                 32                  5          0.00208772         128                                              │
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   32                 64                  3          0.00128416         116                                              │
│ trial_5c837    RUNNING              3   rmsprop         relu                                  128                 32                  3          0.000155082         64                                              │
│ trial_5c837    RUNNING              2   rmsprop         tanh                                   64                128                  3          0.000337639         72                                              │
│ trial_5c837    RUNNING              2   adam            tanh                                   64                 64                  3          0.0016925          119                                              │
│ trial_5c837    RUNNING              2   adam            tanh                                   64                 64                  3          0.000425983         69                                              │
│ trial_5c837    RUNNING              4   adam            tanh                                  128                 32                  3          3.88891e-05        139                                              │
│ trial_5c837    RUNNING              2   adam            relu                                   64                 64                  5          0.000136214        110                                              │
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   64                 64                  3          7.35601e-05        147                                              │
│ trial_5c837    RUNNING              3   rmsprop         relu                                   32                 64                  5          0.00284314         125                                              │
│ trial_5c837    RUNNING              4   rmsprop         tanh                                   32                 32                  3          0.00284271         103                                              │
│ trial_5c837    RUNNING              4   rmsprop         relu                                  128                 32                  5          1.39284e-05         80                                              │
│ trial_5c837    RUNNING              4   adam            relu                                   64                 32                  3          0.000166824         57                                              │
│ trial_5c837    RUNNING              2   adam            tanh                                   32                 64                  5          0.00326602          91                                              │
│ trial_5c837    RUNNING              4   adam            tanh                                   64                 32                  5          0.000133042        102                                              │
│ trial_5c837    RUNNING              2   rmsprop         tanh                                   64                128                  3          0.00419881         110                                              │
│ trial_5c837    RUNNING              4   adam            tanh                                   32                 32                  3          0.00235056         142                                              │
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   32                 32                  3          5.54287e-05        123                                              │
│ trial_5c837    TERMINATED           2   adam            relu                                  128                 32                  5          0.000203836        127        1            298.758         0.276411 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                  128                128                  3          0.00123868          97        1            356.744         0.350786 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2076654)[0m Epoch 56/64[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m Epoch 57/64[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
[1m 65/146[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m3s[0m 39ms/step - accuracy: 0.0742 - loss: 3.0760
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
[1m103/584[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m10s[0m 23ms/step - accuracy: 0.1009 - loss: 2.7088
[1m106/584[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m10s[0m 23ms/step - accuracy: 0.1007 - loss: 2.7090[32m [repeated 104x across cluster][0m
[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m51s[0m 89ms/step - accuracy: 0.0938 - loss: 2.8182[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=2076638)[0m 
[1m129/584[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m10s[0m 23ms/step - accuracy: 0.2554 - loss: 2.0530
[1m132/584[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m10s[0m 23ms/step - accuracy: 0.2550 - loss: 2.0534
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[36m(train_cnn_ray_tune pid=2076625)[0m 
[1m129/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m4s[0m 30ms/step - accuracy: 0.1350 - loss: 2.5715
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[1m133/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m4s[0m 30ms/step - accuracy: 0.1349 - loss: 2.5715[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m Epoch 36/110[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=2076659)[0m 
[1m  3/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 26ms/step - accuracy: 0.2656 - loss: 1.9709  
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
[1m104/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m4s[0m 24ms/step - accuracy: 0.2921 - loss: 1.9401[32m [repeated 250x across cluster][0m
[36m(train_cnn_ray_tune pid=2076656)[0m 
[1m 32/146[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m4s[0m 38ms/step - accuracy: 0.0692 - loss: 3.1074
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m Epoch 23/91[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m Epoch 49/139[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
[1m126/169[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 13ms/step
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[36m(train_cnn_ray_tune pid=2076668)[0m 
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m5s[0m 29ms/step - accuracy: 0.1745 - loss: 2.3138[32m [repeated 233x across cluster][0m
[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
[1m  3/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 28ms/step - accuracy: 0.1727 - loss: 2.2852 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m 
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[36m(train_cnn_ray_tune pid=2076645)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454
[36m(train_cnn_ray_tune pid=2076645)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2076645)[0m 
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Trial trial_5c837 finished iteration 1 at 2025-10-28 13:54:35. Total running time: 6min 24s
╭──────────────────────────────────────╮
│ Trial trial_5c837 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             381.673 │
│ time_total_s                 381.673 │
│ training_iteration                 1 │
│ val_accuracy                 0.19056 │
╰──────────────────────────────────────╯

Trial trial_5c837 completed after 1 iterations at 2025-10-28 13:54:35. Total running time: 6min 24s
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m Epoch 50/80[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
[1m1

Trial status: 3 TERMINATED | 17 RUNNING
Current time: 2025-10-28 13:54:42. Total running time: 6min 31s
Logical resource usage: 17.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_5c837    RUNNING              3   adam            relu                                   32                 32                  5          0.00208772         128                                              │
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   32                 64                  3          0.00128416         116                                              │
│ trial_5c837    RUNNING              3   rmsprop         relu                                  128                 32                  3          0.000155082         64                                              │
│ trial_5c837    RUNNING              2   rmsprop         tanh                                   64                128                  3          0.000337639         72                                              │
│ trial_5c837    RUNNING              2   adam            tanh                                   64                 64                  3          0.0016925          119                                              │
│ trial_5c837    RUNNING              2   adam            tanh                                   64                 64                  3          0.000425983         69                                              │
│ trial_5c837    RUNNING              4   adam            tanh                                  128                 32                  3          3.88891e-05        139                                              │
│ trial_5c837    RUNNING              2   adam            relu                                   64                 64                  5          0.000136214        110                                              │
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   64                 64                  3          7.35601e-05        147                                              │
│ trial_5c837    RUNNING              3   rmsprop         relu                                   32                 64                  5          0.00284314         125                                              │
│ trial_5c837    RUNNING              4   rmsprop         relu                                  128                 32                  5          1.39284e-05         80                                              │
│ trial_5c837    RUNNING              4   adam            relu                                   64                 32                  3          0.000166824         57                                              │
│ trial_5c837    RUNNING              2   adam            tanh                                   32                 64                  5          0.00326602          91                                              │
│ trial_5c837    RUNNING              4   adam            tanh                                   64                 32                  5          0.000133042        102                                              │
│ trial_5c837    RUNNING              2   rmsprop         tanh                                   64                128                  3          0.00419881         110                                              │
│ trial_5c837    RUNNING              4   adam            tanh                                   32                 32                  3          0.00235056         142                                              │
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   32                 32                  3          5.54287e-05        123                                              │
│ trial_5c837    TERMINATED           2   adam            relu                                  128                 32                  5          0.000203836        127        1            298.758         0.276411 │
│ trial_5c837    TERMINATED           4   rmsprop         tanh                                   32                 32                  3          0.00284271         103        1            381.673         0.190564 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                  128                128                  3          0.00123868          97        1            356.744         0.350786 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2076656)[0m Epoch 51/80[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m Epoch 41/119[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m Epoch 19/142[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m Epoch 53/139[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
[1m 9/86[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 7ms/step   
[1m14/86[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 9ms/step
[36m(train_cnn_ray_tune pid=2076654)[0m 
[1m18/86[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 10ms/step
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
[1m34/86[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 10ms/step
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[1m63/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 10ms/step
[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
[1m83/86[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 10ms/step
[36m(train_cnn_ray_tune pid=2076654)[0m 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
[36m(train_cnn_ray_tune pid=2076659)[0m 
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m2s[0m 23ms/step - accuracy: 0.2498 - loss: 2.0610[32m [repeated 282x across cluster][0m
[36m(train_cnn_ray_tune pid=2076631)[0m 
[1m284/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m7s[0m 26ms/step - accuracy: 0.1681 - loss: 2.3770
[1m286/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m7s[0m 26ms/step - accuracy: 0.1681 - loss: 2.3770[32m [repeated 395x across cluster][0m
[36m(train_cnn_ray_tune pid=2076654)[0m 
[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 42ms/step
[36m(train_cnn_ray_tune pid=2076654)[0m 
[1m  7/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step 
[1m 13/169[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step
[36m(train_cnn_ray_tune pid=2076654)[0m 
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[1m 24/169[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 10ms/step
[36m(train_cnn_ray_tune pid=2076654)[0m 
[1m 30/169[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 10ms/step
[36m(train_cnn_ray_tune pid=2076655)[0m 
[1m150/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m9s[0m 23ms/step - accuracy: 0.2316 - loss: 2.0937 
[1m153/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m9s[0m 23ms/step - accuracy: 0.2316 - loss: 2.0938[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
[1m 36/169[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 10ms/step
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
[1m 61/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step 
[36m(train_cnn_ray_tune pid=2076665)[0m 
[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 86ms/step - accuracy: 0.2656 - loss: 2.0881
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454
[36m(train_cnn_ray_tune pid=2076654)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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[36m(train_cnn_ray_tune pid=2076654)[0m 
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Trial trial_5c837 finished iteration 1 at 2025-10-28 13:55:02. Total running time: 6min 50s
╭──────────────────────────────────────╮
│ Trial trial_5c837 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             407.594 │
│ time_total_s                 407.594 │
│ training_iteration                 1 │
│ val_accuracy                 0.24496 │
╰──────────────────────────────────────╯

Trial trial_5c837 completed after 1 iterations at 2025-10-28 13:55:02. Total running time: 6min 50s
[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m Epoch 54/139[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2076659)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m 
[1m 11/169[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 10ms/step
[1m 18/169[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step 
[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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Trial trial_5c837 finished iteration 1 at 2025-10-28 13:55:06. Total running time: 6min 55s
[36m(train_cnn_ray_tune pid=2076659)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454
[36m(train_cnn_ray_tune pid=2076659)[0m   _log_deprecation_warning(
╭──────────────────────────────────────╮
│ Trial trial_5c837 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             412.193 │
│ time_total_s                 412.193 │
│ training_iteration                 1 │
│ val_accuracy                 0.27919 │
╰──────────────────────────────────────╯

Trial trial_5c837 completed after 1 iterations at 2025-10-28 13:55:06. Total running time: 6min 55s
[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m Epoch 26/91[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=2076659)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076659)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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Trial status: 5 TERMINATED | 15 RUNNING
Current time: 2025-10-28 13:55:12. Total running time: 7min 1s
Logical resource usage: 15.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_5c837    RUNNING              3   adam            relu                                   32                 32                  5          0.00208772         128                                              │
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   32                 64                  3          0.00128416         116                                              │
│ trial_5c837    RUNNING              2   rmsprop         tanh                                   64                128                  3          0.000337639         72                                              │
│ trial_5c837    RUNNING              2   adam            tanh                                   64                 64                  3          0.000425983         69                                              │
│ trial_5c837    RUNNING              4   adam            tanh                                  128                 32                  3          3.88891e-05        139                                              │
│ trial_5c837    RUNNING              2   adam            relu                                   64                 64                  5          0.000136214        110                                              │
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   64                 64                  3          7.35601e-05        147                                              │
│ trial_5c837    RUNNING              3   rmsprop         relu                                   32                 64                  5          0.00284314         125                                              │
│ trial_5c837    RUNNING              4   rmsprop         relu                                  128                 32                  5          1.39284e-05         80                                              │
│ trial_5c837    RUNNING              4   adam            relu                                   64                 32                  3          0.000166824         57                                              │
│ trial_5c837    RUNNING              2   adam            tanh                                   32                 64                  5          0.00326602          91                                              │
│ trial_5c837    RUNNING              4   adam            tanh                                   64                 32                  5          0.000133042        102                                              │
│ trial_5c837    RUNNING              2   rmsprop         tanh                                   64                128                  3          0.00419881         110                                              │
│ trial_5c837    RUNNING              4   adam            tanh                                   32                 32                  3          0.00235056         142                                              │
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   32                 32                  3          5.54287e-05        123                                              │
│ trial_5c837    TERMINATED           2   adam            relu                                  128                 32                  5          0.000203836        127        1            298.758         0.276411 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                  128                 32                  3          0.000155082         64        1            407.594         0.244958 │
│ trial_5c837    TERMINATED           2   adam            tanh                                   64                 64                  3          0.0016925          119        1            412.193         0.279186 │
│ trial_5c837    TERMINATED           4   rmsprop         tanh                                   32                 32                  3          0.00284271         103        1            381.673         0.190564 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                  128                128                  3          0.00123868          97        1            356.744         0.350786 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2076670)[0m 
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[1m290/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m4s[0m 15ms/step - accuracy: 0.2457 - loss: 2.1284
[1m294/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m4s[0m 15ms/step - accuracy: 0.2456 - loss: 2.1284[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=2076657)[0m 
[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 67ms/step - accuracy: 0.3594 - loss: 1.9767
[1m  5/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 16ms/step - accuracy: 0.3065 - loss: 1.9938 [32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=2076667)[0m Epoch 56/139[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m Epoch 35/147[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m Epoch 24/116[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
[1m  1/146[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 74ms/step - accuracy: 0.0938 - loss: 3.0041
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[36m(train_cnn_ray_tune pid=2076670)[0m Epoch 28/91[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m Epoch 37/147[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m Epoch 61/139[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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Trial status: 5 TERMINATED | 15 RUNNING
Current time: 2025-10-28 13:55:42. Total running time: 7min 31s
Logical resource usage: 15.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_5c837    RUNNING              3   adam            relu                                   32                 32                  5          0.00208772         128                                              │
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   32                 64                  3          0.00128416         116                                              │
│ trial_5c837    RUNNING              2   rmsprop         tanh                                   64                128                  3          0.000337639         72                                              │
│ trial_5c837    RUNNING              2   adam            tanh                                   64                 64                  3          0.000425983         69                                              │
│ trial_5c837    RUNNING              4   adam            tanh                                  128                 32                  3          3.88891e-05        139                                              │
│ trial_5c837    RUNNING              2   adam            relu                                   64                 64                  5          0.000136214        110                                              │
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   64                 64                  3          7.35601e-05        147                                              │
│ trial_5c837    RUNNING              3   rmsprop         relu                                   32                 64                  5          0.00284314         125                                              │
│ trial_5c837    RUNNING              4   rmsprop         relu                                  128                 32                  5          1.39284e-05         80                                              │
│ trial_5c837    RUNNING              4   adam            relu                                   64                 32                  3          0.000166824         57                                              │
│ trial_5c837    RUNNING              2   adam            tanh                                   32                 64                  5          0.00326602          91                                              │
│ trial_5c837    RUNNING              4   adam            tanh                                   64                 32                  5          0.000133042        102                                              │
│ trial_5c837    RUNNING              2   rmsprop         tanh                                   64                128                  3          0.00419881         110                                              │
│ trial_5c837    RUNNING              4   adam            tanh                                   32                 32                  3          0.00235056         142                                              │
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   32                 32                  3          5.54287e-05        123                                              │
│ trial_5c837    TERMINATED           2   adam            relu                                  128                 32                  5          0.000203836        127        1            298.758         0.276411 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                  128                 32                  3          0.000155082         64        1            407.594         0.244958 │
│ trial_5c837    TERMINATED           2   adam            tanh                                   64                 64                  3          0.0016925          119        1            412.193         0.279186 │
│ trial_5c837    TERMINATED           4   rmsprop         tanh                                   32                 32                  3          0.00284271         103        1            381.673         0.190564 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                  128                128                  3          0.00123868          97        1            356.744         0.350786 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m Epoch 39/102[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m Epoch 39/147[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=2076657)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454
[36m(train_cnn_ray_tune pid=2076657)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2076670)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454
[36m(train_cnn_ray_tune pid=2076670)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[36m(train_cnn_ray_tune pid=2076657)[0m 
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[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step

Trial trial_5c837 finished iteration 1 at 2025-10-28 13:55:49. Total running time: 7min 37s
╭──────────────────────────────────────╮
│ Trial trial_5c837 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             455.001 │
│ time_total_s                 455.001 │
│ training_iteration                 1 │
│ val_accuracy                 0.28936 │
╰──────────────────────────────────────╯

Trial trial_5c837 completed after 1 iterations at 2025-10-28 13:55:49. Total running time: 7min 37s
[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
[1m 10/169[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 6ms/step 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
[1m 34/146[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m3s[0m 27ms/step - accuracy: 0.0840 - loss: 2.9516[32m [repeated 166x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
[1m153/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m6s[0m 15ms/step - accuracy: 0.1312 - loss: 2.6423
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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Trial trial_5c837 finished iteration 1 at 2025-10-28 13:55:51. Total running time: 7min 40s
╭──────────────────────────────────────╮
│ Trial trial_5c837 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             456.979 │
│ time_total_s                 456.979 │
│ training_iteration                 1 │
│ val_accuracy                 0.29343 │
╰──────────────────────────────────────╯

Trial trial_5c837 completed after 1 iterations at 2025-10-28 13:55:51. Total running time: 7min 40s
[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076670)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m Epoch 41/110[32m [repeated 8x across cluster][0m
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m Epoch 29/123[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m Epoch 42/102[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m Epoch 28/116[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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Trial status: 7 TERMINATED | 13 RUNNING
Current time: 2025-10-28 13:56:12. Total running time: 8min 1s
Logical resource usage: 13.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_5c837    RUNNING              3   adam            relu                                   32                 32                  5          0.00208772         128                                              │
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   32                 64                  3          0.00128416         116                                              │
│ trial_5c837    RUNNING              2   rmsprop         tanh                                   64                128                  3          0.000337639         72                                              │
│ trial_5c837    RUNNING              4   adam            tanh                                  128                 32                  3          3.88891e-05        139                                              │
│ trial_5c837    RUNNING              2   adam            relu                                   64                 64                  5          0.000136214        110                                              │
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   64                 64                  3          7.35601e-05        147                                              │
│ trial_5c837    RUNNING              3   rmsprop         relu                                   32                 64                  5          0.00284314         125                                              │
│ trial_5c837    RUNNING              4   rmsprop         relu                                  128                 32                  5          1.39284e-05         80                                              │
│ trial_5c837    RUNNING              4   adam            relu                                   64                 32                  3          0.000166824         57                                              │
│ trial_5c837    RUNNING              4   adam            tanh                                   64                 32                  5          0.000133042        102                                              │
│ trial_5c837    RUNNING              2   rmsprop         tanh                                   64                128                  3          0.00419881         110                                              │
│ trial_5c837    RUNNING              4   adam            tanh                                   32                 32                  3          0.00235056         142                                              │
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   32                 32                  3          5.54287e-05        123                                              │
│ trial_5c837    TERMINATED           2   adam            relu                                  128                 32                  5          0.000203836        127        1            298.758         0.276411 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                  128                 32                  3          0.000155082         64        1            407.594         0.244958 │
│ trial_5c837    TERMINATED           2   adam            tanh                                   64                 64                  3          0.0016925          119        1            412.193         0.279186 │
│ trial_5c837    TERMINATED           2   adam            tanh                                   64                 64                  3          0.000425983         69        1            455.001         0.289362 │
│ trial_5c837    TERMINATED           4   rmsprop         tanh                                   32                 32                  3          0.00284271         103        1            381.673         0.190564 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                  128                128                  3          0.00123868          97        1            356.744         0.350786 │
│ trial_5c837    TERMINATED           2   adam            tanh                                   32                 64                  5          0.00326602          91        1            456.979         0.293432 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2076631)[0m 
[1m  4/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 18ms/step - accuracy: 0.1198 - loss: 2.4645
[1m  8/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 18ms/step - accuracy: 0.1398 - loss: 2.4429
[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m Epoch 43/57[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m Epoch 71/139[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m Epoch 26/142[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m Epoch 56/110[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454
[36m(train_cnn_ray_tune pid=2076666)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2076666)[0m 
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[36m(train_cnn_ray_tune pid=2076666)[0m 
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Trial trial_5c837 finished iteration 1 at 2025-10-28 13:56:30. Total running time: 8min 19s
╭──────────────────────────────────────╮
│ Trial trial_5c837 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             496.325 │
│ time_total_s                 496.325 │
│ training_iteration                 1 │
│ val_accuracy                 0.27271 │
╰──────────────────────────────────────╯

Trial trial_5c837 completed after 1 iterations at 2025-10-28 13:56:30. Total running time: 8min 19s
[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m43s[0m 74ms/step - accuracy: 0.1562 - loss: 2.0522[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m Epoch 75/80[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m Epoch 31/116[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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Trial status: 8 TERMINATED | 12 RUNNING
Current time: 2025-10-28 13:56:42. Total running time: 8min 31s
Logical resource usage: 12.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_5c837    RUNNING              3   adam            relu                                   32                 32                  5          0.00208772         128                                              │
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   32                 64                  3          0.00128416         116                                              │
│ trial_5c837    RUNNING              4   adam            tanh                                  128                 32                  3          3.88891e-05        139                                              │
│ trial_5c837    RUNNING              2   adam            relu                                   64                 64                  5          0.000136214        110                                              │
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   64                 64                  3          7.35601e-05        147                                              │
│ trial_5c837    RUNNING              3   rmsprop         relu                                   32                 64                  5          0.00284314         125                                              │
│ trial_5c837    RUNNING              4   rmsprop         relu                                  128                 32                  5          1.39284e-05         80                                              │
│ trial_5c837    RUNNING              4   adam            relu                                   64                 32                  3          0.000166824         57                                              │
│ trial_5c837    RUNNING              4   adam            tanh                                   64                 32                  5          0.000133042        102                                              │
│ trial_5c837    RUNNING              2   rmsprop         tanh                                   64                128                  3          0.00419881         110                                              │
│ trial_5c837    RUNNING              4   adam            tanh                                   32                 32                  3          0.00235056         142                                              │
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   32                 32                  3          5.54287e-05        123                                              │
│ trial_5c837    TERMINATED           2   adam            relu                                  128                 32                  5          0.000203836        127        1            298.758         0.276411 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                  128                 32                  3          0.000155082         64        1            407.594         0.244958 │
│ trial_5c837    TERMINATED           2   rmsprop         tanh                                   64                128                  3          0.000337639         72        1            496.325         0.27271  │
│ trial_5c837    TERMINATED           2   adam            tanh                                   64                 64                  3          0.0016925          119        1            412.193         0.279186 │
│ trial_5c837    TERMINATED           2   adam            tanh                                   64                 64                  3          0.000425983         69        1            455.001         0.289362 │
│ trial_5c837    TERMINATED           4   rmsprop         tanh                                   32                 32                  3          0.00284271         103        1            381.673         0.190564 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                  128                128                  3          0.00123868          97        1            356.744         0.350786 │
│ trial_5c837    TERMINATED           2   adam            tanh                                   32                 64                  5          0.00326602          91        1            456.979         0.293432 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m Epoch 31/125[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
[1m  4/146[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 20ms/step - accuracy: 0.1047 - loss: 2.6677 
[1m  7/146[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 21ms/step - accuracy: 0.0955 - loss: 2.6839[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=2076656)[0m 
[1m146/146[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 24ms/step - accuracy: 0.0803 - loss: 2.9354 - val_accuracy: 0.1378 - val_loss: 2.5949[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2076619)[0m 
[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 56ms/step - accuracy: 0.1250 - loss: 2.4191[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m Epoch 79/139[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m Epoch 51/110[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454
[36m(train_cnn_ray_tune pid=2076656)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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[36m(train_cnn_ray_tune pid=2076656)[0m 
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Trial trial_5c837 finished iteration 1 at 2025-10-28 13:56:57. Total running time: 8min 46s
╭──────────────────────────────────────╮
│ Trial trial_5c837 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             522.925 │
│ time_total_s                 522.925 │
│ training_iteration                 1 │
│ val_accuracy                 0.13636 │
╰──────────────────────────────────────╯

Trial trial_5c837 completed after 1 iterations at 2025-10-28 13:56:57. Total running time: 8min 46s
[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m Epoch 52/110[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m 
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[36m(train_cnn_ray_tune pid=2076671)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454
[36m(train_cnn_ray_tune pid=2076671)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2076631)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454
[36m(train_cnn_ray_tune pid=2076631)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2076671)[0m 
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[1m165/169[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 4ms/step
[36m(train_cnn_ray_tune pid=2076671)[0m 
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
[36m(train_cnn_ray_tune pid=2076631)[0m 
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[1m11/86[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 5ms/step   

Trial trial_5c837 finished iteration 1 at 2025-10-28 13:57:04. Total running time: 8min 52s
╭──────────────────────────────────────╮
│ Trial trial_5c837 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             529.977 │
│ time_total_s                 529.977 │
│ training_iteration                 1 │
│ val_accuracy                 0.31952 │
╰──────────────────────────────────────╯

Trial trial_5c837 completed after 1 iterations at 2025-10-28 13:57:04. Total running time: 8min 52s
[36m(train_cnn_ray_tune pid=2076665)[0m Epoch 53/110[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
[1m152/584[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m4s[0m 12ms/step - accuracy: 0.1382 - loss: 2.5768
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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Trial trial_5c837 finished iteration 1 at 2025-10-28 13:57:06. Total running time: 8min 54s
╭──────────────────────────────────────╮
│ Trial trial_5c837 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             532.006 │
│ time_total_s                 532.006 │
│ training_iteration                 1 │
│ val_accuracy                 0.19852 │
╰──────────────────────────────────────╯

Trial trial_5c837 completed after 1 iterations at 2025-10-28 13:57:06. Total running time: 8min 54s
[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076631)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m Epoch 54/110[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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Trial status: 11 TERMINATED | 9 RUNNING
Current time: 2025-10-28 13:57:12. Total running time: 9min 1s
Logical resource usage: 9.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_5c837    RUNNING              3   adam            relu                                   32                 32                  5          0.00208772         128                                              │
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   32                 64                  3          0.00128416         116                                              │
│ trial_5c837    RUNNING              4   adam            tanh                                  128                 32                  3          3.88891e-05        139                                              │
│ trial_5c837    RUNNING              2   adam            relu                                   64                 64                  5          0.000136214        110                                              │
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   64                 64                  3          7.35601e-05        147                                              │
│ trial_5c837    RUNNING              4   adam            relu                                   64                 32                  3          0.000166824         57                                              │
│ trial_5c837    RUNNING              4   adam            tanh                                   64                 32                  5          0.000133042        102                                              │
│ trial_5c837    RUNNING              2   rmsprop         tanh                                   64                128                  3          0.00419881         110                                              │
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   32                 32                  3          5.54287e-05        123                                              │
│ trial_5c837    TERMINATED           2   adam            relu                                  128                 32                  5          0.000203836        127        1            298.758         0.276411 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                  128                 32                  3          0.000155082         64        1            407.594         0.244958 │
│ trial_5c837    TERMINATED           2   rmsprop         tanh                                   64                128                  3          0.000337639         72        1            496.325         0.27271  │
│ trial_5c837    TERMINATED           2   adam            tanh                                   64                 64                  3          0.0016925          119        1            412.193         0.279186 │
│ trial_5c837    TERMINATED           2   adam            tanh                                   64                 64                  3          0.000425983         69        1            455.001         0.289362 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                   32                 64                  5          0.00284314         125        1            529.977         0.319519 │
│ trial_5c837    TERMINATED           4   rmsprop         tanh                                   32                 32                  3          0.00284271         103        1            381.673         0.190564 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                  128                128                  3          0.00123868          97        1            356.744         0.350786 │
│ trial_5c837    TERMINATED           4   rmsprop         relu                                  128                 32                  5          1.39284e-05         80        1            522.925         0.136355 │
│ trial_5c837    TERMINATED           2   adam            tanh                                   32                 64                  5          0.00326602          91        1            456.979         0.293432 │
│ trial_5c837    TERMINATED           4   adam            tanh                                   32                 32                  3          0.00235056         142        1            532.006         0.19852  │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2076669)[0m Epoch 67/110[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
[1m225/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m3s[0m 9ms/step - accuracy: 0.1385 - loss: 2.5855
[1m232/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m3s[0m 9ms/step - accuracy: 0.1385 - loss: 2.5854[32m [repeated 115x across cluster][0m
[36m(train_cnn_ray_tune pid=2076619)[0m 
[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 56ms/step - accuracy: 0.1406 - loss: 2.3672
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 12ms/step - accuracy: 0.1789 - loss: 2.3882
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 14ms/step - accuracy: 0.2071 - loss: 2.1603 - val_accuracy: 0.2235 - val_loss: 1.9452[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[1m207/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 9ms/step - accuracy: 0.3376 - loss: 1.7615
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[36m(train_cnn_ray_tune pid=2076625)[0m Epoch 56/102[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m Epoch 37/116[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m Epoch 39/128[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454
[36m(train_cnn_ray_tune pid=2076668)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2076638)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454
[36m(train_cnn_ray_tune pid=2076638)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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[36m(train_cnn_ray_tune pid=2076668)[0m 
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Trial trial_5c837 finished iteration 1 at 2025-10-28 13:57:34. Total running time: 9min 23s
╭──────────────────────────────────────╮
│ Trial trial_5c837 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             560.641 │
│ time_total_s                 560.641 │
│ training_iteration                 1 │
│ val_accuracy                 0.22516 │
╰──────────────────────────────────────╯

Trial trial_5c837 completed after 1 iterations at 2025-10-28 13:57:34. Total running time: 9min 23s
[36m(train_cnn_ray_tune pid=2076669)[0m Epoch 73/110[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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Trial trial_5c837 finished iteration 1 at 2025-10-28 13:57:37. Total running time: 9min 25s
╭──────────────────────────────────────╮
│ Trial trial_5c837 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             562.817 │
│ time_total_s                 562.817 │
│ training_iteration                 1 │
│ val_accuracy                 0.24107 │
╰──────────────────────────────────────╯

Trial trial_5c837 completed after 1 iterations at 2025-10-28 13:57:37. Total running time: 9min 25s
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076638)[0m 
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=2076667)[0m Epoch 98/139[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[1m 18/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 6ms/step - accuracy: 0.1715 - loss: 2.5661[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=2076669)[0m 
[1m 33/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 7ms/step - accuracy: 0.3663 - loss: 1.7360[32m [repeated 19x across cluster][0m

Trial status: 13 TERMINATED | 7 RUNNING
Current time: 2025-10-28 13:57:43. Total running time: 9min 31s
Logical resource usage: 7.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   32                 64                  3          0.00128416         116                                              │
│ trial_5c837    RUNNING              4   adam            tanh                                  128                 32                  3          3.88891e-05        139                                              │
│ trial_5c837    RUNNING              2   adam            relu                                   64                 64                  5          0.000136214        110                                              │
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   64                 64                  3          7.35601e-05        147                                              │
│ trial_5c837    RUNNING              4   adam            tanh                                   64                 32                  5          0.000133042        102                                              │
│ trial_5c837    RUNNING              2   rmsprop         tanh                                   64                128                  3          0.00419881         110                                              │
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   32                 32                  3          5.54287e-05        123                                              │
│ trial_5c837    TERMINATED           2   adam            relu                                  128                 32                  5          0.000203836        127        1            298.758         0.276411 │
│ trial_5c837    TERMINATED           3   adam            relu                                   32                 32                  5          0.00208772         128        1            562.817         0.241073 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                  128                 32                  3          0.000155082         64        1            407.594         0.244958 │
│ trial_5c837    TERMINATED           2   rmsprop         tanh                                   64                128                  3          0.000337639         72        1            496.325         0.27271  │
│ trial_5c837    TERMINATED           2   adam            tanh                                   64                 64                  3          0.0016925          119        1            412.193         0.279186 │
│ trial_5c837    TERMINATED           2   adam            tanh                                   64                 64                  3          0.000425983         69        1            455.001         0.289362 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                   32                 64                  5          0.00284314         125        1            529.977         0.319519 │
│ trial_5c837    TERMINATED           4   rmsprop         tanh                                   32                 32                  3          0.00284271         103        1            381.673         0.190564 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                  128                128                  3          0.00123868          97        1            356.744         0.350786 │
│ trial_5c837    TERMINATED           4   rmsprop         relu                                  128                 32                  5          1.39284e-05         80        1            522.925         0.136355 │
│ trial_5c837    TERMINATED           4   adam            relu                                   64                 32                  3          0.000166824         57        1            560.641         0.225162 │
│ trial_5c837    TERMINATED           2   adam            tanh                                   32                 64                  5          0.00326602          91        1            456.979         0.293432 │
│ trial_5c837    TERMINATED           4   adam            tanh                                   32                 32                  3          0.00235056         142        1            532.006         0.19852  │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2076667)[0m 
[1m146/146[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 13ms/step - accuracy: 0.1258 - loss: 2.6326 - val_accuracy: 0.1371 - val_loss: 2.4993[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
[1m219/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 9ms/step - accuracy: 0.1888 - loss: 2.3522 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m Epoch 79/110[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m Epoch 46/123[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
[1m290/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 9ms/step - accuracy: 0.2615 - loss: 2.0233[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m Epoch 67/147[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
[1m 21/146[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 11ms/step - accuracy: 0.1294 - loss: 2.5945
[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
[1m146/146[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 13ms/step - accuracy: 0.1314 - loss: 2.5964 - val_accuracy: 0.1421 - val_loss: 2.4790[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=2076655)[0m 
[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m27s[0m 47ms/step - accuracy: 0.1875 - loss: 2.0683[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=2076669)[0m Epoch 85/110[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=2076669)[0m 
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[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 7ms/step - accuracy: 0.3509 - loss: 1.7508[32m [repeated 195x across cluster][0m
[36m(train_cnn_ray_tune pid=2076669)[0m 
[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 45ms/step - accuracy: 0.3281 - loss: 1.8080
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[36m(train_cnn_ray_tune pid=2076619)[0m 
[1m  7/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 10ms/step - accuracy: 0.2516 - loss: 2.2039 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 10ms/step - accuracy: 0.2197 - loss: 2.2027 - val_accuracy: 0.2729 - val_loss: 2.0076[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
[1m146/146[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step - accuracy: 0.1255 - loss: 2.6045[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m Epoch 72/110[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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Trial status: 13 TERMINATED | 7 RUNNING
Current time: 2025-10-28 13:58:13. Total running time: 10min 1s
Logical resource usage: 7.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   32                 64                  3          0.00128416         116                                              │
│ trial_5c837    RUNNING              4   adam            tanh                                  128                 32                  3          3.88891e-05        139                                              │
│ trial_5c837    RUNNING              2   adam            relu                                   64                 64                  5          0.000136214        110                                              │
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   64                 64                  3          7.35601e-05        147                                              │
│ trial_5c837    RUNNING              4   adam            tanh                                   64                 32                  5          0.000133042        102                                              │
│ trial_5c837    RUNNING              2   rmsprop         tanh                                   64                128                  3          0.00419881         110                                              │
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   32                 32                  3          5.54287e-05        123                                              │
│ trial_5c837    TERMINATED           2   adam            relu                                  128                 32                  5          0.000203836        127        1            298.758         0.276411 │
│ trial_5c837    TERMINATED           3   adam            relu                                   32                 32                  5          0.00208772         128        1            562.817         0.241073 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                  128                 32                  3          0.000155082         64        1            407.594         0.244958 │
│ trial_5c837    TERMINATED           2   rmsprop         tanh                                   64                128                  3          0.000337639         72        1            496.325         0.27271  │
│ trial_5c837    TERMINATED           2   adam            tanh                                   64                 64                  3          0.0016925          119        1            412.193         0.279186 │
│ trial_5c837    TERMINATED           2   adam            tanh                                   64                 64                  3          0.000425983         69        1            455.001         0.289362 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                   32                 64                  5          0.00284314         125        1            529.977         0.319519 │
│ trial_5c837    TERMINATED           4   rmsprop         tanh                                   32                 32                  3          0.00284271         103        1            381.673         0.190564 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                  128                128                  3          0.00123868          97        1            356.744         0.350786 │
│ trial_5c837    TERMINATED           4   rmsprop         relu                                  128                 32                  5          1.39284e-05         80        1            522.925         0.136355 │
│ trial_5c837    TERMINATED           4   adam            relu                                   64                 32                  3          0.000166824         57        1            560.641         0.225162 │
│ trial_5c837    TERMINATED           2   adam            tanh                                   32                 64                  5          0.00326602          91        1            456.979         0.293432 │
│ trial_5c837    TERMINATED           4   adam            tanh                                   32                 32                  3          0.00235056         142        1            532.006         0.19852  │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m Epoch 116/139[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454
[36m(train_cnn_ray_tune pid=2076665)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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[36m(train_cnn_ray_tune pid=2076665)[0m 
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Trial trial_5c837 finished iteration 1 at 2025-10-28 13:58:23. Total running time: 10min 12s
╭──────────────────────────────────────╮
│ Trial trial_5c837 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             609.121 │
│ time_total_s                 609.121 │
│ training_iteration                 1 │
│ val_accuracy                 0.31082 │
╰──────────────────────────────────────╯

Trial trial_5c837 completed after 1 iterations at 2025-10-28 13:58:23. Total running time: 10min 12s
[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m Epoch 93/110[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=2076669)[0m 
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[36m(train_cnn_ray_tune pid=2076669)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454
[36m(train_cnn_ray_tune pid=2076669)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m Epoch 124/139[32m [repeated 10x across cluster][0m
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Trial trial_5c837 finished iteration 1 at 2025-10-28 13:58:30. Total running time: 10min 19s
╭──────────────────────────────────────╮
│ Trial trial_5c837 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             616.216 │
│ time_total_s                 616.216 │
│ training_iteration                 1 │
│ val_accuracy                  0.3617 │
╰──────────────────────────────────────╯

Trial trial_5c837 completed after 1 iterations at 2025-10-28 13:58:30. Total running time: 10min 19s
[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m Epoch 82/147[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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Trial status: 15 TERMINATED | 5 RUNNING
Current time: 2025-10-28 13:58:43. Total running time: 10min 31s
Logical resource usage: 5.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   32                 64                  3          0.00128416         116                                              │
│ trial_5c837    RUNNING              4   adam            tanh                                  128                 32                  3          3.88891e-05        139                                              │
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   64                 64                  3          7.35601e-05        147                                              │
│ trial_5c837    RUNNING              4   adam            tanh                                   64                 32                  5          0.000133042        102                                              │
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   32                 32                  3          5.54287e-05        123                                              │
│ trial_5c837    TERMINATED           2   adam            relu                                  128                 32                  5          0.000203836        127        1            298.758         0.276411 │
│ trial_5c837    TERMINATED           3   adam            relu                                   32                 32                  5          0.00208772         128        1            562.817         0.241073 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                  128                 32                  3          0.000155082         64        1            407.594         0.244958 │
│ trial_5c837    TERMINATED           2   rmsprop         tanh                                   64                128                  3          0.000337639         72        1            496.325         0.27271  │
│ trial_5c837    TERMINATED           2   adam            tanh                                   64                 64                  3          0.0016925          119        1            412.193         0.279186 │
│ trial_5c837    TERMINATED           2   adam            tanh                                   64                 64                  3          0.000425983         69        1            455.001         0.289362 │
│ trial_5c837    TERMINATED           2   adam            relu                                   64                 64                  5          0.000136214        110        1            616.216         0.361702 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                   32                 64                  5          0.00284314         125        1            529.977         0.319519 │
│ trial_5c837    TERMINATED           4   rmsprop         tanh                                   32                 32                  3          0.00284271         103        1            381.673         0.190564 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                  128                128                  3          0.00123868          97        1            356.744         0.350786 │
│ trial_5c837    TERMINATED           4   rmsprop         relu                                  128                 32                  5          1.39284e-05         80        1            522.925         0.136355 │
│ trial_5c837    TERMINATED           4   adam            relu                                   64                 32                  3          0.000166824         57        1            560.641         0.225162 │
│ trial_5c837    TERMINATED           2   adam            tanh                                   32                 64                  5          0.00326602          91        1            456.979         0.293432 │
│ trial_5c837    TERMINATED           2   rmsprop         tanh                                   64                128                  3          0.00419881         110        1            609.121         0.310823 │
│ trial_5c837    TERMINATED           4   adam            tanh                                   32                 32                  3          0.00235056         142        1            532.006         0.19852  │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2076667)[0m 
[1m142/146[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 8ms/step - accuracy: 0.1449 - loss: 2.5477[32m [repeated 16x across cluster][0m
[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
[1m  8/146[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 7ms/step - accuracy: 0.1571 - loss: 2.5498 
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[36m(train_cnn_ray_tune pid=2076655)[0m Epoch 54/116[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454
[36m(train_cnn_ray_tune pid=2076667)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2076625)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454
[36m(train_cnn_ray_tune pid=2076625)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2076625)[0m 
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[36m(train_cnn_ray_tune pid=2076667)[0m 
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[36m(train_cnn_ray_tune pid=2076625)[0m 
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Trial trial_5c837 finished iteration 1 at 2025-10-28 13:58:52. Total running time: 10min 41s
╭──────────────────────────────────────╮
│ Trial trial_5c837 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             638.505 │
│ time_total_s                 638.505 │
│ training_iteration                 1 │
│ val_accuracy                 0.22035 │
╰──────────────────────────────────────╯

Trial trial_5c837 completed after 1 iterations at 2025-10-28 13:58:52. Total running time: 10min 41s

Trial trial_5c837 finished iteration 1 at 2025-10-28 13:58:52. Total running time: 10min 41s
╭──────────────────────────────────────╮
│ Trial trial_5c837 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             638.736 │
│ time_total_s                 638.736 │
│ training_iteration                 1 │
│ val_accuracy                 0.17188 │
╰──────────────────────────────────────╯

Trial trial_5c837 completed after 1 iterations at 2025-10-28 13:58:52. Total running time: 10min 41s
[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m Epoch 57/116[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 30ms/step - accuracy: 0.3438 - loss: 1.8423[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=2076655)[0m 
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[1m 27/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 4ms/step - accuracy: 0.2657 - loss: 1.9721
[36m(train_cnn_ray_tune pid=2076655)[0m Epoch 59/116[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 31ms/step - accuracy: 0.1562 - loss: 2.3858
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[36m(train_cnn_ray_tune pid=2076658)[0m 
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 4ms/step - accuracy: 0.1660 - loss: 2.4589 - val_accuracy: 0.2094 - val_loss: 2.2833[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
[1m105/584[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.2791 - loss: 2.0122[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2076619)[0m 
[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 32ms/step - accuracy: 0.2969 - loss: 2.0405[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
[1m 17/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 3ms/step - accuracy: 0.1313 - loss: 2.5080  
[1m 32/584[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 3ms/step - accuracy: 0.1448 - loss: 2.4845[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m Epoch 66/123[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
[1m 79/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.2425 - loss: 2.0078
[1m 93/584[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.2431 - loss: 2.0057[32m [repeated 111x across cluster][0m
[36m(train_cnn_ray_tune pid=2076655)[0m 
[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 27ms/step - accuracy: 0.3125 - loss: 1.8942
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[36m(train_cnn_ray_tune pid=2076658)[0m 
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 4ms/step - accuracy: 0.1716 - loss: 2.4480 - val_accuracy: 0.1882 - val_loss: 2.2732[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=2076619)[0m 
[1m 12/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 5ms/step - accuracy: 0.2526 - loss: 2.1115 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
[1m289/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 5ms/step - accuracy: 0.2390 - loss: 2.1068[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2076655)[0m 
[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 28ms/step - accuracy: 0.2500 - loss: 2.1326[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=2076655)[0m 
[1m 14/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 4ms/step - accuracy: 0.2776 - loss: 2.0296  
[1m 28/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 4ms/step - accuracy: 0.2765 - loss: 2.0229[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=2076619)[0m 
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[1m 25/292[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.2310 - loss: 2.1154
[36m(train_cnn_ray_tune pid=2076619)[0m Epoch 99/147[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=2076619)[0m 
[1m 85/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 4ms/step - accuracy: 0.2346 - loss: 2.1154
[1m 97/292[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 4ms/step - accuracy: 0.2352 - loss: 2.1150[32m [repeated 105x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 31ms/step - accuracy: 0.3125 - loss: 2.0339
[1m 16/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 4ms/step - accuracy: 0.2196 - loss: 2.3212  [32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 4ms/step - accuracy: 0.1750 - loss: 2.4263 - val_accuracy: 0.2054 - val_loss: 2.2432[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=2076619)[0m 
[1m133/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 5ms/step - accuracy: 0.2337 - loss: 2.1116
[1m144/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 5ms/step - accuracy: 0.2338 - loss: 2.1117
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Trial status: 17 TERMINATED | 3 RUNNING
Current time: 2025-10-28 13:59:13. Total running time: 11min 1s
Logical resource usage: 3.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   32                 64                  3          0.00128416         116                                              │
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   64                 64                  3          7.35601e-05        147                                              │
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   32                 32                  3          5.54287e-05        123                                              │
│ trial_5c837    TERMINATED           2   adam            relu                                  128                 32                  5          0.000203836        127        1            298.758         0.276411 │
│ trial_5c837    TERMINATED           3   adam            relu                                   32                 32                  5          0.00208772         128        1            562.817         0.241073 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                  128                 32                  3          0.000155082         64        1            407.594         0.244958 │
│ trial_5c837    TERMINATED           2   rmsprop         tanh                                   64                128                  3          0.000337639         72        1            496.325         0.27271  │
│ trial_5c837    TERMINATED           2   adam            tanh                                   64                 64                  3          0.0016925          119        1            412.193         0.279186 │
│ trial_5c837    TERMINATED           2   adam            tanh                                   64                 64                  3          0.000425983         69        1            455.001         0.289362 │
│ trial_5c837    TERMINATED           4   adam            tanh                                  128                 32                  3          3.88891e-05        139        1            638.736         0.171878 │
│ trial_5c837    TERMINATED           2   adam            relu                                   64                 64                  5          0.000136214        110        1            616.216         0.361702 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                   32                 64                  5          0.00284314         125        1            529.977         0.319519 │
│ trial_5c837    TERMINATED           4   rmsprop         tanh                                   32                 32                  3          0.00284271         103        1            381.673         0.190564 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                  128                128                  3          0.00123868          97        1            356.744         0.350786 │
│ trial_5c837    TERMINATED           4   rmsprop         relu                                  128                 32                  5          1.39284e-05         80        1            522.925         0.136355 │
│ trial_5c837    TERMINATED           4   adam            relu                                   64                 32                  3          0.000166824         57        1            560.641         0.225162 │
│ trial_5c837    TERMINATED           2   adam            tanh                                   32                 64                  5          0.00326602          91        1            456.979         0.293432 │
│ trial_5c837    TERMINATED           4   adam            tanh                                   64                 32                  5          0.000133042        102        1            638.505         0.220352 │
│ trial_5c837    TERMINATED           2   rmsprop         tanh                                   64                128                  3          0.00419881         110        1            609.121         0.310823 │
│ trial_5c837    TERMINATED           4   adam            tanh                                   32                 32                  3          0.00235056         142        1            532.006         0.19852  │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m Epoch 73/123[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454
[36m(train_cnn_ray_tune pid=2076655)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076655)[0m 
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Trial trial_5c837 finished iteration 1 at 2025-10-28 13:59:30. Total running time: 11min 19s
╭──────────────────────────────────────╮
│ Trial trial_5c837 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             676.102 │
│ time_total_s                 676.102 │
│ training_iteration                 1 │
│ val_accuracy                 0.28085 │
╰──────────────────────────────────────╯

Trial trial_5c837 completed after 1 iterations at 2025-10-28 13:59:30. Total running time: 11min 19s
[36m(train_cnn_ray_tune pid=2076655)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m Epoch 111/147[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m Epoch 115/147[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m Epoch 82/123[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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Trial status: 18 TERMINATED | 2 RUNNING
Current time: 2025-10-28 13:59:43. Total running time: 11min 31s
Logical resource usage: 2.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   64                 64                  3          7.35601e-05        147                                              │
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   32                 32                  3          5.54287e-05        123                                              │
│ trial_5c837    TERMINATED           2   adam            relu                                  128                 32                  5          0.000203836        127        1            298.758         0.276411 │
│ trial_5c837    TERMINATED           3   adam            relu                                   32                 32                  5          0.00208772         128        1            562.817         0.241073 │
│ trial_5c837    TERMINATED           3   rmsprop         tanh                                   32                 64                  3          0.00128416         116        1            676.102         0.280851 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                  128                 32                  3          0.000155082         64        1            407.594         0.244958 │
│ trial_5c837    TERMINATED           2   rmsprop         tanh                                   64                128                  3          0.000337639         72        1            496.325         0.27271  │
│ trial_5c837    TERMINATED           2   adam            tanh                                   64                 64                  3          0.0016925          119        1            412.193         0.279186 │
│ trial_5c837    TERMINATED           2   adam            tanh                                   64                 64                  3          0.000425983         69        1            455.001         0.289362 │
│ trial_5c837    TERMINATED           4   adam            tanh                                  128                 32                  3          3.88891e-05        139        1            638.736         0.171878 │
│ trial_5c837    TERMINATED           2   adam            relu                                   64                 64                  5          0.000136214        110        1            616.216         0.361702 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                   32                 64                  5          0.00284314         125        1            529.977         0.319519 │
│ trial_5c837    TERMINATED           4   rmsprop         tanh                                   32                 32                  3          0.00284271         103        1            381.673         0.190564 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                  128                128                  3          0.00123868          97        1            356.744         0.350786 │
│ trial_5c837    TERMINATED           4   rmsprop         relu                                  128                 32                  5          1.39284e-05         80        1            522.925         0.136355 │
│ trial_5c837    TERMINATED           4   adam            relu                                   64                 32                  3          0.000166824         57        1            560.641         0.225162 │
│ trial_5c837    TERMINATED           2   adam            tanh                                   32                 64                  5          0.00326602          91        1            456.979         0.293432 │
│ trial_5c837    TERMINATED           4   adam            tanh                                   64                 32                  5          0.000133042        102        1            638.505         0.220352 │
│ trial_5c837    TERMINATED           2   rmsprop         tanh                                   64                128                  3          0.00419881         110        1            609.121         0.310823 │
│ trial_5c837    TERMINATED           4   adam            tanh                                   32                 32                  3          0.00235056         142        1            532.006         0.19852  │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m Epoch 122/147[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m Epoch 87/123[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m Epoch 129/147[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=2076619)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454
[36m(train_cnn_ray_tune pid=2076619)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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[36m(train_cnn_ray_tune pid=2076619)[0m 
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Trial trial_5c837 finished iteration 1 at 2025-10-28 13:59:59. Total running time: 11min 48s
╭──────────────────────────────────────╮
│ Trial trial_5c837 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             705.176 │
│ time_total_s                 705.176 │
│ training_iteration                 1 │
│ val_accuracy                 0.27364 │
╰──────────────────────────────────────╯

Trial trial_5c837 completed after 1 iterations at 2025-10-28 13:59:59. Total running time: 11min 48s
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m Epoch 92/123[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m Epoch 95/123[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m Epoch 98/123[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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Trial status: 19 TERMINATED | 1 RUNNING
Current time: 2025-10-28 14:00:13. Total running time: 12min 1s
Logical resource usage: 1.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   32                 32                  3          5.54287e-05        123                                              │
│ trial_5c837    TERMINATED           2   adam            relu                                  128                 32                  5          0.000203836        127        1            298.758         0.276411 │
│ trial_5c837    TERMINATED           3   adam            relu                                   32                 32                  5          0.00208772         128        1            562.817         0.241073 │
│ trial_5c837    TERMINATED           3   rmsprop         tanh                                   32                 64                  3          0.00128416         116        1            676.102         0.280851 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                  128                 32                  3          0.000155082         64        1            407.594         0.244958 │
│ trial_5c837    TERMINATED           2   rmsprop         tanh                                   64                128                  3          0.000337639         72        1            496.325         0.27271  │
│ trial_5c837    TERMINATED           2   adam            tanh                                   64                 64                  3          0.0016925          119        1            412.193         0.279186 │
│ trial_5c837    TERMINATED           2   adam            tanh                                   64                 64                  3          0.000425983         69        1            455.001         0.289362 │
│ trial_5c837    TERMINATED           4   adam            tanh                                  128                 32                  3          3.88891e-05        139        1            638.736         0.171878 │
│ trial_5c837    TERMINATED           2   adam            relu                                   64                 64                  5          0.000136214        110        1            616.216         0.361702 │
│ trial_5c837    TERMINATED           3   rmsprop         tanh                                   64                 64                  3          7.35601e-05        147        1            705.176         0.273636 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                   32                 64                  5          0.00284314         125        1            529.977         0.319519 │
│ trial_5c837    TERMINATED           4   rmsprop         tanh                                   32                 32                  3          0.00284271         103        1            381.673         0.190564 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                  128                128                  3          0.00123868          97        1            356.744         0.350786 │
│ trial_5c837    TERMINATED           4   rmsprop         relu                                  128                 32                  5          1.39284e-05         80        1            522.925         0.136355 │
│ trial_5c837    TERMINATED           4   adam            relu                                   64                 32                  3          0.000166824         57        1            560.641         0.225162 │
│ trial_5c837    TERMINATED           2   adam            tanh                                   32                 64                  5          0.00326602          91        1            456.979         0.293432 │
│ trial_5c837    TERMINATED           4   adam            tanh                                   64                 32                  5          0.000133042        102        1            638.505         0.220352 │
│ trial_5c837    TERMINATED           2   rmsprop         tanh                                   64                128                  3          0.00419881         110        1            609.121         0.310823 │
│ trial_5c837    TERMINATED           4   adam            tanh                                   32                 32                  3          0.00235056         142        1            532.006         0.19852  │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m Epoch 101/123[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m Epoch 107/123[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m Epoch 110/123[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m Epoch 113/123[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m Epoch 116/123[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
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Trial status: 19 TERMINATED | 1 RUNNING
Current time: 2025-10-28 14:00:43. Total running time: 12min 31s
Logical resource usage: 1.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_5c837    RUNNING              3   rmsprop         tanh                                   32                 32                  3          5.54287e-05        123                                              │
│ trial_5c837    TERMINATED           2   adam            relu                                  128                 32                  5          0.000203836        127        1            298.758         0.276411 │
│ trial_5c837    TERMINATED           3   adam            relu                                   32                 32                  5          0.00208772         128        1            562.817         0.241073 │
│ trial_5c837    TERMINATED           3   rmsprop         tanh                                   32                 64                  3          0.00128416         116        1            676.102         0.280851 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                  128                 32                  3          0.000155082         64        1            407.594         0.244958 │
│ trial_5c837    TERMINATED           2   rmsprop         tanh                                   64                128                  3          0.000337639         72        1            496.325         0.27271  │
│ trial_5c837    TERMINATED           2   adam            tanh                                   64                 64                  3          0.0016925          119        1            412.193         0.279186 │
│ trial_5c837    TERMINATED           2   adam            tanh                                   64                 64                  3          0.000425983         69        1            455.001         0.289362 │
│ trial_5c837    TERMINATED           4   adam            tanh                                  128                 32                  3          3.88891e-05        139        1            638.736         0.171878 │
│ trial_5c837    TERMINATED           2   adam            relu                                   64                 64                  5          0.000136214        110        1            616.216         0.361702 │
│ trial_5c837    TERMINATED           3   rmsprop         tanh                                   64                 64                  3          7.35601e-05        147        1            705.176         0.273636 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                   32                 64                  5          0.00284314         125        1            529.977         0.319519 │
│ trial_5c837    TERMINATED           4   rmsprop         tanh                                   32                 32                  3          0.00284271         103        1            381.673         0.190564 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                  128                128                  3          0.00123868          97        1            356.744         0.350786 │
│ trial_5c837    TERMINATED           4   rmsprop         relu                                  128                 32                  5          1.39284e-05         80        1            522.925         0.136355 │
│ trial_5c837    TERMINATED           4   adam            relu                                   64                 32                  3          0.000166824         57        1            560.641         0.225162 │
│ trial_5c837    TERMINATED           2   adam            tanh                                   32                 64                  5          0.00326602          91        1            456.979         0.293432 │
│ trial_5c837    TERMINATED           4   adam            tanh                                   64                 32                  5          0.000133042        102        1            638.505         0.220352 │
│ trial_5c837    TERMINATED           2   rmsprop         tanh                                   64                128                  3          0.00419881         110        1            609.121         0.310823 │
│ trial_5c837    TERMINATED           4   adam            tanh                                   32                 32                  3          0.00235056         142        1            532.006         0.19852  │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
[1m356/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1994 - loss: 2.2952
[36m(train_cnn_ray_tune pid=2076658)[0m 
[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1875 - loss: 2.1069
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[36m(train_cnn_ray_tune pid=2076658)[0m Epoch 119/123[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m Epoch 122/123[32m [repeated 3x across cluster][0m
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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Trial trial_5c837 finished iteration 1 at 2025-10-28 14:00:56. Total running time: 12min 45s
[36m(train_cnn_ray_tune pid=2076658)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454
[36m(train_cnn_ray_tune pid=2076658)[0m   _log_deprecation_warning(
2025-10-28 14:00:56,493	INFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_C/case_C_ESANN_acc_gyr_17_classes/ESANN_hyperparameters_tuning' in 0.0073s.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1761656456.634152 2075027 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
╭──────────────────────────────────────╮
│ Trial trial_5c837 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             762.133 │
│ time_total_s                 762.133 │
│ training_iteration                 1 │
│ val_accuracy                  0.2383 │
╰──────────────────────────────────────╯

Trial trial_5c837 completed after 1 iterations at 2025-10-28 14:00:56. Total running time: 12min 45s
[36m(train_cnn_ray_tune pid=2076658)[0m 
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Trial status: 20 TERMINATED
Current time: 2025-10-28 14:00:56. Total running time: 12min 45s
Logical resource usage: 1.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_5c837    TERMINATED           2   adam            relu                                  128                 32                  5          0.000203836        127        1            298.758         0.276411 │
│ trial_5c837    TERMINATED           3   adam            relu                                   32                 32                  5          0.00208772         128        1            562.817         0.241073 │
│ trial_5c837    TERMINATED           3   rmsprop         tanh                                   32                 64                  3          0.00128416         116        1            676.102         0.280851 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                  128                 32                  3          0.000155082         64        1            407.594         0.244958 │
│ trial_5c837    TERMINATED           2   rmsprop         tanh                                   64                128                  3          0.000337639         72        1            496.325         0.27271  │
│ trial_5c837    TERMINATED           2   adam            tanh                                   64                 64                  3          0.0016925          119        1            412.193         0.279186 │
│ trial_5c837    TERMINATED           2   adam            tanh                                   64                 64                  3          0.000425983         69        1            455.001         0.289362 │
│ trial_5c837    TERMINATED           4   adam            tanh                                  128                 32                  3          3.88891e-05        139        1            638.736         0.171878 │
│ trial_5c837    TERMINATED           2   adam            relu                                   64                 64                  5          0.000136214        110        1            616.216         0.361702 │
│ trial_5c837    TERMINATED           3   rmsprop         tanh                                   64                 64                  3          7.35601e-05        147        1            705.176         0.273636 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                   32                 64                  5          0.00284314         125        1            529.977         0.319519 │
│ trial_5c837    TERMINATED           4   rmsprop         tanh                                   32                 32                  3          0.00284271         103        1            381.673         0.190564 │
│ trial_5c837    TERMINATED           3   rmsprop         relu                                  128                128                  3          0.00123868          97        1            356.744         0.350786 │
│ trial_5c837    TERMINATED           4   rmsprop         relu                                  128                 32                  5          1.39284e-05         80        1            522.925         0.136355 │
│ trial_5c837    TERMINATED           4   adam            relu                                   64                 32                  3          0.000166824         57        1            560.641         0.225162 │
│ trial_5c837    TERMINATED           2   adam            tanh                                   32                 64                  5          0.00326602          91        1            456.979         0.293432 │
│ trial_5c837    TERMINATED           4   adam            tanh                                   64                 32                  5          0.000133042        102        1            638.505         0.220352 │
│ trial_5c837    TERMINATED           2   rmsprop         tanh                                   64                128                  3          0.00419881         110        1            609.121         0.310823 │
│ trial_5c837    TERMINATED           4   adam            tanh                                   32                 32                  3          0.00235056         142        1            532.006         0.19852  │
│ trial_5c837    TERMINATED           3   rmsprop         tanh                                   32                 32                  3          5.54287e-05        123        1            762.133         0.238298 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Mejores hiperparámetros: {'N_capas': 2, 'optimizador': 'adam', 'funcion_activacion': 'relu', 'tamanho_minilote': 64, 'numero_filtros': 64, 'tamanho_filtro': 5, 'tasa_aprendizaje': 0.00013621354548739694, 'epochs': 110}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/110
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761656458.537087 2200671 service.cc:152] XLA service 0x7c78e8005310 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761656458.537157 2200671 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 14:00:58.575271: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
I0000 00:00:1761656458.755643 2200671 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761656461.058667 2200671 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/110

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Epoch 3/110

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Epoch 4/110

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Epoch 5/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1342 - loss: 2.8712 
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1334 - loss: 2.8557
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1334 - loss: 2.8522
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1334 - loss: 2.8491 - val_accuracy: 0.2061 - val_loss: 2.3390
Epoch 6/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1327 - loss: 2.7676 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1337 - loss: 2.7650
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[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1364 - loss: 2.7545
[1m259/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1370 - loss: 2.7515
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Epoch 7/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1450 - loss: 2.7348 
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1528 - loss: 2.6839 - val_accuracy: 0.2309 - val_loss: 2.2588
Epoch 8/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1467 - loss: 2.6535 
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Epoch 9/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1566 - loss: 2.5690 
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[1m217/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1633 - loss: 2.5504
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1650 - loss: 2.5456 - val_accuracy: 0.2455 - val_loss: 2.1767
Epoch 10/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1250 - loss: 2.5876
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1706 - loss: 2.4734 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1719 - loss: 2.4801
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[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1719 - loss: 2.4838
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1717 - loss: 2.4837
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1718 - loss: 2.4830
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1721 - loss: 2.4819
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1722 - loss: 2.4812 - val_accuracy: 0.2548 - val_loss: 2.1396
Epoch 11/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1250 - loss: 2.4678
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1664 - loss: 2.4693 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1698 - loss: 2.4633
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1725 - loss: 2.4584
[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1742 - loss: 2.4562
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1758 - loss: 2.4536
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1767 - loss: 2.4515
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1773 - loss: 2.4495
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1776 - loss: 2.4482 - val_accuracy: 0.2660 - val_loss: 2.1107
Epoch 12/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.2188 - loss: 2.3289
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2126 - loss: 2.3430 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2031 - loss: 2.3636
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[1m147/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1980 - loss: 2.3762
[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1970 - loss: 2.3779
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1965 - loss: 2.3785
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1959 - loss: 2.3791
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Epoch 13/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1971 - loss: 2.4033 
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1952 - loss: 2.3632
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1953 - loss: 2.3605
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Epoch 14/110

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[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2015 - loss: 2.3156
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Epoch 15/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1875 - loss: 2.3109
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[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2093 - loss: 2.2732
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2095 - loss: 2.2726 - val_accuracy: 0.2840 - val_loss: 1.9718
Epoch 16/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2500 - loss: 2.0981
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1939 - loss: 2.2819 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2002 - loss: 2.2777
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[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2058 - loss: 2.2671
[1m199/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2073 - loss: 2.2629
[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2082 - loss: 2.2593
[1m275/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2087 - loss: 2.2567
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2088 - loss: 2.2558 - val_accuracy: 0.2740 - val_loss: 1.9481
Epoch 17/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 24ms/step - accuracy: 0.2031 - loss: 2.1821
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2030 - loss: 2.2223 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2045 - loss: 2.2208
[1m111/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2066 - loss: 2.2212
[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2082 - loss: 2.2228
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2091 - loss: 2.2243
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2099 - loss: 2.2241
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2104 - loss: 2.2235
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2107 - loss: 2.2230 - val_accuracy: 0.2895 - val_loss: 1.9342
Epoch 18/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1562 - loss: 2.3378
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2045 - loss: 2.2362 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2054 - loss: 2.2295
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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2114 - loss: 2.2132
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2122 - loss: 2.2112
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Epoch 19/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2656 - loss: 2.2123
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2254 - loss: 2.1820 
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Epoch 20/110

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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2188 - loss: 2.1658 - val_accuracy: 0.3021 - val_loss: 1.8747
Epoch 21/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2500 - loss: 2.2399
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2274 - loss: 2.1307 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2255 - loss: 2.1269
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[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2269 - loss: 2.1260
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2274 - loss: 2.1272
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2277 - loss: 2.1287
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2282 - loss: 2.1297
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2286 - loss: 2.1300 - val_accuracy: 0.2875 - val_loss: 1.8547
Epoch 22/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2188 - loss: 1.9223
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2502 - loss: 2.0733 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2463 - loss: 2.0902
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2452 - loss: 2.0952
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2443 - loss: 2.1003
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2437 - loss: 2.1038
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2431 - loss: 2.1059
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2426 - loss: 2.1076
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2423 - loss: 2.1082 - val_accuracy: 0.2890 - val_loss: 1.8600
Epoch 23/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.1719 - loss: 2.1514
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2354 - loss: 2.0811 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2414 - loss: 2.0829
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[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2445 - loss: 2.0840
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2444 - loss: 2.0863
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2443 - loss: 2.0880
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2444 - loss: 2.0895
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Epoch 24/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2546 - loss: 2.0632 
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Epoch 25/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2398 - loss: 2.0905 
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Epoch 26/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2544 - loss: 2.0691 
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[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2531 - loss: 2.0719
[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2530 - loss: 2.0707
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2528 - loss: 2.0688 - val_accuracy: 0.3177 - val_loss: 1.8102
Epoch 27/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2580 - loss: 2.0252 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2549 - loss: 2.0323
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[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2549 - loss: 2.0387
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2548 - loss: 2.0404
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2551 - loss: 2.0411
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2556 - loss: 2.0410
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.2561 - loss: 2.0408
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2561 - loss: 2.0408 - val_accuracy: 0.3154 - val_loss: 1.8030
Epoch 28/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2664 - loss: 2.0035 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2633 - loss: 2.0141
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2620 - loss: 2.0226
[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2614 - loss: 2.0265
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2615 - loss: 2.0284
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2615 - loss: 2.0285
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2614 - loss: 2.0288
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2613 - loss: 2.0293 - val_accuracy: 0.3077 - val_loss: 1.7992
Epoch 29/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2505 - loss: 2.0721 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2578 - loss: 2.0488
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2627 - loss: 2.0325
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Epoch 30/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2514 - loss: 2.0120 
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Epoch 31/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2500 - loss: 1.9779
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2672 - loss: 2.0108 
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2670 - loss: 2.0102
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2672 - loss: 2.0097
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2672 - loss: 2.0096 - val_accuracy: 0.3223 - val_loss: 1.7890
Epoch 32/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2500 - loss: 1.9629
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2746 - loss: 2.0012 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2711 - loss: 2.0030
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2713 - loss: 2.0019
[1m145/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2711 - loss: 2.0012
[1m182/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2712 - loss: 2.0008
[1m219/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2709 - loss: 2.0005
[1m257/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2706 - loss: 2.0003
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2704 - loss: 2.0003 - val_accuracy: 0.3347 - val_loss: 1.7824
Epoch 33/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2344 - loss: 1.9399
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2804 - loss: 1.9459 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2738 - loss: 1.9622
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2726 - loss: 1.9650
[1m143/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2727 - loss: 1.9669
[1m180/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2725 - loss: 1.9701
[1m217/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2722 - loss: 1.9730
[1m254/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2724 - loss: 1.9745
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.2727 - loss: 1.9756
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2727 - loss: 1.9756 - val_accuracy: 0.3334 - val_loss: 1.7631
Epoch 34/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1875 - loss: 2.2675
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2681 - loss: 2.0098 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2700 - loss: 2.0064
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2741 - loss: 1.9966
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2769 - loss: 1.9903
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2781 - loss: 1.9878 - val_accuracy: 0.3301 - val_loss: 1.7610
Epoch 35/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 25ms/step - accuracy: 0.2344 - loss: 2.0012
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2850 - loss: 1.9727 
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[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2850 - loss: 1.9779
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Epoch 36/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2788 - loss: 1.9888 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2810 - loss: 1.9815
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[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2847 - loss: 1.9732
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2854 - loss: 1.9711
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2855 - loss: 1.9700
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2854 - loss: 1.9696 - val_accuracy: 0.3293 - val_loss: 1.7490
Epoch 37/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3281 - loss: 1.9581
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2878 - loss: 1.9738 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2872 - loss: 1.9789
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[1m147/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2874 - loss: 1.9754
[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2870 - loss: 1.9737
[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2867 - loss: 1.9716
[1m258/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2867 - loss: 1.9700
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2868 - loss: 1.9687 - val_accuracy: 0.3423 - val_loss: 1.7567
Epoch 38/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2656 - loss: 2.0778
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3121 - loss: 1.9518 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3020 - loss: 1.9497
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2986 - loss: 1.9475
[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2966 - loss: 1.9468
[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2958 - loss: 1.9463
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2952 - loss: 1.9461
[1m262/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2945 - loss: 1.9465
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2941 - loss: 1.9467 - val_accuracy: 0.3378 - val_loss: 1.7376
Epoch 39/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.2969 - loss: 1.8771
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2880 - loss: 1.9783 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2883 - loss: 1.9683
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[1m144/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2904 - loss: 1.9533
[1m181/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2910 - loss: 1.9500
[1m220/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2912 - loss: 1.9487
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2912 - loss: 1.9475
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2912 - loss: 1.9464 - val_accuracy: 0.3410 - val_loss: 1.7258
Epoch 40/110

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Epoch 41/110

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Epoch 42/110

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Epoch 43/110

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Epoch 44/110

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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3144 - loss: 1.8964
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Epoch 45/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2982 - loss: 1.9043 
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Epoch 46/110

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Epoch 47/110

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Epoch 48/110

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[1m 34/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3088 - loss: 1.8888 
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[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3116 - loss: 1.8862
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.3121 - loss: 1.8858
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3121 - loss: 1.8858 - val_accuracy: 0.3512 - val_loss: 1.6828
Epoch 49/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2918 - loss: 1.9106 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3018 - loss: 1.8876
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[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3055 - loss: 1.8787
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3058 - loss: 1.8783
[1m220/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3059 - loss: 1.8786
[1m258/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3061 - loss: 1.8785
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3064 - loss: 1.8786 - val_accuracy: 0.3606 - val_loss: 1.6911
Epoch 50/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3107 - loss: 1.8365 
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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3151 - loss: 1.8603
[1m275/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3149 - loss: 1.8618
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Epoch 51/110

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Epoch 52/110

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Epoch 53/110

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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3210 - loss: 1.8448
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3205 - loss: 1.8464 - val_accuracy: 0.3539 - val_loss: 1.6682
Epoch 54/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3085 - loss: 1.8740 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3149 - loss: 1.8568
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3199 - loss: 1.8437
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3198 - loss: 1.8434
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Epoch 55/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3125 - loss: 2.0681
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3189 - loss: 1.8881 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3199 - loss: 1.8654
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[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3194 - loss: 1.8561
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3197 - loss: 1.8539
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3198 - loss: 1.8526
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Epoch 56/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3329 - loss: 1.8698 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3338 - loss: 1.8573
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Epoch 57/110

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Epoch 58/110

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Epoch 59/110

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[1m291/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3217 - loss: 1.8298
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3217 - loss: 1.8298 - val_accuracy: 0.3556 - val_loss: 1.6526
Epoch 60/110

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Epoch 61/110

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Epoch 62/110

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Epoch 63/110

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Epoch 64/110

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Epoch 65/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3357 - loss: 1.7829 
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Epoch 66/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3330 - loss: 1.8225 
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Epoch 67/110

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Epoch 68/110

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Epoch 69/110

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Epoch 70/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3262 - loss: 1.8250 
[1m 70/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3284 - loss: 1.8196
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[1m210/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3334 - loss: 1.8057
[1m245/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3341 - loss: 1.8039
[1m279/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3344 - loss: 1.8021
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3346 - loss: 1.8015 - val_accuracy: 0.3608 - val_loss: 1.6240
Epoch 71/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3169 - loss: 1.8424 
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[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3283 - loss: 1.8232
[1m144/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3302 - loss: 1.8161
[1m182/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3317 - loss: 1.8095
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Epoch 72/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3682 - loss: 1.7725 
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Epoch 73/110

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Epoch 74/110

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Saved model to disk.
[36m(train_cnn_ray_tune pid=2076658)[0m Epoch 123/123
[36m(train_cnn_ray_tune pid=2076658)[0m 
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[36m(train_cnn_ray_tune pid=2076658)[0m 
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=== EJECUCIÓN 1 ===

--- TRAIN (ejecución 1) ---

--- TEST (ejecución 1) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18676, 6, 250)

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[1m135/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 753us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 36.98 [%]
Global F1 score (validation) = 36.73 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[2.0710270e-01 1.8399300e-01 2.1999688e-01 ... 5.6645536e-06
  1.5424655e-01 2.8877353e-02]
 [2.0243014e-01 1.8874089e-01 2.1880999e-01 ... 1.0046111e-05
  1.5092669e-01 2.7734885e-02]
 [2.0028299e-01 2.0470278e-01 1.9892746e-01 ... 1.8726831e-05
  1.5330628e-01 1.1277593e-02]
 ...
 [1.7424725e-01 2.2156395e-01 1.8495552e-01 ... 1.1274410e-04
  1.9301972e-01 8.9661181e-03]
 [1.8738408e-01 2.1928364e-01 1.9704254e-01 ... 6.5543900e-05
  1.8270266e-01 9.6597588e-03]
 [8.4486336e-02 1.2799536e-01 8.6967967e-02 ... 7.8351022e-04
  1.0799165e-01 1.7474950e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 43.94 [%]
Global accuracy score (test) = 37.6 [%]
Global F1 score (train) = 42.64 [%]
Global F1 score (test) = 36.87 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.23      0.24      0.24       184
 CAMINAR CON MÓVIL O LIBRO       0.17      0.32      0.22       184
       CAMINAR USUAL SPEED       0.31      0.24      0.27       184
            CAMINAR ZIGZAG       0.27      0.21      0.24       184
          DE PIE BARRIENDO       0.24      0.43      0.31       184
   DE PIE DOBLANDO TOALLAS       0.18      0.08      0.11       184
    DE PIE MOVIENDO LIBROS       0.32      0.37      0.35       184
          DE PIE USANDO PC       0.47      0.60      0.53       184
        FASE REPOSO CON K5       0.61      0.86      0.71       184
INCREMENTAL CICLOERGOMETRO       0.89      0.59      0.71       184
           SENTADO LEYENDO       0.36      0.50      0.42       184
         SENTADO USANDO PC       0.45      0.14      0.21       184
      SENTADO VIENDO LA TV       0.37      0.30      0.34       184
   SUBIR Y BAJAR ESCALERAS       0.23      0.08      0.11       184
                    TROTAR       0.86      0.71      0.78       161

                  accuracy                           0.38      2737
                 macro avg       0.40      0.38      0.37      2737
              weighted avg       0.39      0.38      0.37      2737

2025-10-28 14:02:01.425651: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 14:02:01.437007: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1761656521.450413 2208470 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761656521.454710 2208470 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761656521.464831 2208470 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761656521.464852 2208470 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761656521.464854 2208470 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761656521.464856 2208470 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 14:02:01.468098: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1761656523.860234 2208470 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/110
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761656525.584699 2208599 service.cc:152] XLA service 0x71b12401e180 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761656525.584740 2208599 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 14:02:05.618633: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
I0000 00:00:1761656525.783602 2208599 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761656528.037548 2208599 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16:37[0m 3s/step - accuracy: 0.0938 - loss: 3.4795
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[1m139/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0802 - loss: 3.5008
[1m174/292[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0813 - loss: 3.4852
[1m214/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0825 - loss: 3.4687
[1m254/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0836 - loss: 3.4550
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - accuracy: 0.0847 - loss: 3.4428
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 16ms/step - accuracy: 0.0847 - loss: 3.4425 - val_accuracy: 0.1563 - val_loss: 2.4768
Epoch 2/110

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[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1085 - loss: 3.1867
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1080 - loss: 3.1848
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1077 - loss: 3.1809
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1084 - loss: 3.1722
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1092 - loss: 3.1627 - val_accuracy: 0.1560 - val_loss: 2.4075
Epoch 3/110

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[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1311 - loss: 3.0196
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Epoch 4/110

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Epoch 5/110

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Epoch 6/110

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Epoch 7/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1568 - loss: 2.6869 
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[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1555 - loss: 2.6669
[1m198/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1551 - loss: 2.6647
[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1547 - loss: 2.6622
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1545 - loss: 2.6601
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Epoch 8/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1652 - loss: 2.5799 
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1614 - loss: 2.5882
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Epoch 9/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1655 - loss: 2.5479 
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[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1652 - loss: 2.5366
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1651 - loss: 2.5329 - val_accuracy: 0.2357 - val_loss: 2.1508
Epoch 10/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1720 - loss: 2.4946 
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1764 - loss: 2.4739
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1767 - loss: 2.4722 - val_accuracy: 0.2376 - val_loss: 2.1188
Epoch 11/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 26ms/step - accuracy: 0.2500 - loss: 2.2140
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1814 - loss: 2.4263 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1822 - loss: 2.4237
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[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1838 - loss: 2.4179
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[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1836 - loss: 2.4168
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1835 - loss: 2.4168
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1835 - loss: 2.4169 - val_accuracy: 0.2559 - val_loss: 2.0805
Epoch 12/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.1719 - loss: 2.3984
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1874 - loss: 2.3820 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1878 - loss: 2.3823
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1876 - loss: 2.3830
[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1882 - loss: 2.3810
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1885 - loss: 2.3794
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1884 - loss: 2.3783
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1883 - loss: 2.3772
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1882 - loss: 2.3760 - val_accuracy: 0.2507 - val_loss: 2.0431
Epoch 13/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1250 - loss: 2.3650
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1867 - loss: 2.3396 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1905 - loss: 2.3439
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1923 - loss: 2.3404
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1930 - loss: 2.3394
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1935 - loss: 2.3383
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1941 - loss: 2.3363
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1948 - loss: 2.3344
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1951 - loss: 2.3331 - val_accuracy: 0.2675 - val_loss: 2.0162
Epoch 14/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2188 - loss: 2.1479
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2059 - loss: 2.2892 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2057 - loss: 2.2960
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2054 - loss: 2.2985
[1m144/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2058 - loss: 2.2990
[1m180/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2055 - loss: 2.2996
[1m219/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2052 - loss: 2.3000
[1m254/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2049 - loss: 2.3003
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.2046 - loss: 2.3002
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Epoch 15/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2168 - loss: 2.2666 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2133 - loss: 2.2633
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Epoch 16/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1848 - loss: 2.2592 
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2003 - loss: 2.2458 - val_accuracy: 0.2723 - val_loss: 1.9439
Epoch 17/110

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[1m 34/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2361 - loss: 2.2051 
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[1m182/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2221 - loss: 2.2125
[1m219/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2211 - loss: 2.2128
[1m257/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2206 - loss: 2.2123
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2201 - loss: 2.2119 - val_accuracy: 0.2868 - val_loss: 1.9268
Epoch 18/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1562 - loss: 2.1488
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1941 - loss: 2.2301 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2022 - loss: 2.2212
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2049 - loss: 2.2183
[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2069 - loss: 2.2139
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2084 - loss: 2.2100
[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2092 - loss: 2.2072
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2098 - loss: 2.2052
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2101 - loss: 2.2041 - val_accuracy: 0.2660 - val_loss: 1.9165
Epoch 19/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2656 - loss: 2.1345
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2157 - loss: 2.1766 
[1m 70/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2192 - loss: 2.1654
[1m108/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2210 - loss: 2.1617
[1m147/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2224 - loss: 2.1607
[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2231 - loss: 2.1602
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2232 - loss: 2.1605
[1m263/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2233 - loss: 2.1609
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2234 - loss: 2.1607 - val_accuracy: 0.2825 - val_loss: 1.9007
Epoch 20/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2169 - loss: 2.1748 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2254 - loss: 2.1703
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[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2291 - loss: 2.1630
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2295 - loss: 2.1610
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2298 - loss: 2.1588 - val_accuracy: 0.2623 - val_loss: 1.8879
Epoch 21/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2399 - loss: 2.1527 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2386 - loss: 2.1466
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[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2373 - loss: 2.1424
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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2369 - loss: 2.1393
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Epoch 22/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2275 - loss: 2.1167 
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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2331 - loss: 2.1099
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2338 - loss: 2.1089
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2342 - loss: 2.1086 - val_accuracy: 0.3014 - val_loss: 1.8530
Epoch 23/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3281 - loss: 1.9069
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2624 - loss: 2.0840 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2586 - loss: 2.0888
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[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2536 - loss: 2.0922
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2524 - loss: 2.0926
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2514 - loss: 2.0930
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2505 - loss: 2.0937
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2497 - loss: 2.0940 - val_accuracy: 0.3005 - val_loss: 1.8464
Epoch 24/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2188 - loss: 2.0231
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2337 - loss: 2.0997 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2389 - loss: 2.0931
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2410 - loss: 2.0905
[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2421 - loss: 2.0894
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2428 - loss: 2.0879
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2434 - loss: 2.0863
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2439 - loss: 2.0854
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2440 - loss: 2.0849 - val_accuracy: 0.3080 - val_loss: 1.8319
Epoch 25/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2500 - loss: 2.2392
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2315 - loss: 2.1003 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2348 - loss: 2.0892
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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2396 - loss: 2.0774
[1m181/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2407 - loss: 2.0749
[1m219/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2419 - loss: 2.0727
[1m256/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2426 - loss: 2.0714
[1m287/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2430 - loss: 2.0706
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2431 - loss: 2.0705 - val_accuracy: 0.3056 - val_loss: 1.8323
Epoch 26/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2344 - loss: 2.0916
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2647 - loss: 2.0323 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2612 - loss: 2.0367
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2593 - loss: 2.0381
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2585 - loss: 2.0398
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2574 - loss: 2.0413
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2568 - loss: 2.0427
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2565 - loss: 2.0435
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2564 - loss: 2.0438 - val_accuracy: 0.3079 - val_loss: 1.8134
Epoch 27/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2188 - loss: 1.9086
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2338 - loss: 2.0707 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2422 - loss: 2.0618
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[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2477 - loss: 2.0524
[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2487 - loss: 2.0498
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2493 - loss: 2.0480
[1m259/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2497 - loss: 2.0471
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2502 - loss: 2.0468 - val_accuracy: 0.3217 - val_loss: 1.8046
Epoch 28/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.2031 - loss: 2.2296
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2538 - loss: 2.0500 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2508 - loss: 2.0580
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2502 - loss: 2.0595
[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2509 - loss: 2.0572
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2521 - loss: 2.0542
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2533 - loss: 2.0516
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2541 - loss: 2.0496
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2544 - loss: 2.0484 - val_accuracy: 0.3143 - val_loss: 1.8042
Epoch 29/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3281 - loss: 2.0204
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2716 - loss: 2.0453 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2724 - loss: 2.0329
[1m109/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2719 - loss: 2.0265
[1m144/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2711 - loss: 2.0237
[1m182/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2704 - loss: 2.0215
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2702 - loss: 2.0194
[1m263/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2700 - loss: 2.0180
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2699 - loss: 2.0173 - val_accuracy: 0.3177 - val_loss: 1.7931
Epoch 30/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.2812 - loss: 2.0573
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2490 - loss: 2.0312 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2492 - loss: 2.0266
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2519 - loss: 2.0233
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2549 - loss: 2.0204
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2570 - loss: 2.0189
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2588 - loss: 2.0183
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2600 - loss: 2.0176
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2606 - loss: 2.0170 - val_accuracy: 0.3169 - val_loss: 1.7798
Epoch 31/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2656 - loss: 1.9430
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[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2675 - loss: 2.0090
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Epoch 32/110

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Epoch 33/110

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Epoch 34/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2900 - loss: 1.9941 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2880 - loss: 1.9890
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[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2856 - loss: 1.9756
[1m263/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2853 - loss: 1.9741
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2852 - loss: 1.9733 - val_accuracy: 0.3321 - val_loss: 1.7454
Epoch 35/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2788 - loss: 1.9549 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2788 - loss: 1.9581
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[1m181/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2813 - loss: 1.9596
[1m217/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2824 - loss: 1.9595
[1m255/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2832 - loss: 1.9592
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2834 - loss: 1.9594 - val_accuracy: 0.3397 - val_loss: 1.7381
Epoch 36/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2999 - loss: 1.9945 
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Epoch 37/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2717 - loss: 1.9916 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2807 - loss: 1.9637
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Epoch 38/110

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Epoch 39/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 24ms/step - accuracy: 0.2188 - loss: 1.8295
[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3052 - loss: 1.8893 
[1m 82/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3008 - loss: 1.9018
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2975 - loss: 1.9175
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2974 - loss: 1.9189
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2974 - loss: 1.9194 - val_accuracy: 0.3338 - val_loss: 1.7056
Epoch 40/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3180 - loss: 1.8628 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3105 - loss: 1.8783
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[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3059 - loss: 1.8987
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3049 - loss: 1.9032
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3041 - loss: 1.9062
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3035 - loss: 1.9079
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3032 - loss: 1.9086 - val_accuracy: 0.3299 - val_loss: 1.7056
Epoch 41/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3019 - loss: 1.8760 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3025 - loss: 1.8789
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[1m217/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3049 - loss: 1.8920
[1m253/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3050 - loss: 1.8933
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Epoch 42/110

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Epoch 43/110

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Epoch 44/110

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Epoch 45/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3169 - loss: 1.8590 
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3114 - loss: 1.8694
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Epoch 46/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3089 - loss: 1.9145 
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Epoch 47/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3114 - loss: 1.8746 
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Epoch 48/110

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Epoch 49/110

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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3224 - loss: 1.8596
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Epoch 50/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3245 - loss: 1.8778 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3217 - loss: 1.8716
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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3172 - loss: 1.8660
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3168 - loss: 1.8653
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3166 - loss: 1.8647 - val_accuracy: 0.3597 - val_loss: 1.6629
Epoch 51/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3438 - loss: 1.8243 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3387 - loss: 1.8240
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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3341 - loss: 1.8309
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3320 - loss: 1.8332
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3305 - loss: 1.8351
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3294 - loss: 1.8364
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3288 - loss: 1.8370 - val_accuracy: 0.3543 - val_loss: 1.6432
Epoch 52/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3131 - loss: 1.8601 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3174 - loss: 1.8547
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3192 - loss: 1.8430
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3196 - loss: 1.8421
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Epoch 53/110

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Epoch 54/110

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Epoch 55/110

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Epoch 56/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3323 - loss: 1.8346 
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[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3305 - loss: 1.8282
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Epoch 57/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3335 - loss: 1.8321 
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[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3280 - loss: 1.8242
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Epoch 58/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3160 - loss: 1.8588 
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Epoch 59/110

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Epoch 60/110

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Epoch 61/110

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[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3343 - loss: 1.7902
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3339 - loss: 1.7919 - val_accuracy: 0.3615 - val_loss: 1.6294
Epoch 62/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3237 - loss: 1.7837 
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[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3320 - loss: 1.7890
[1m257/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3325 - loss: 1.7895
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Epoch 63/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3456 - loss: 1.7961 
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Epoch 64/110

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Epoch 65/110

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Epoch 66/110

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Epoch 67/110

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Epoch 68/110

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Epoch 69/110

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Epoch 70/110

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Epoch 71/110

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Epoch 72/110

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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3393 - loss: 1.7608
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3395 - loss: 1.7622 - val_accuracy: 0.3621 - val_loss: 1.6336
Epoch 73/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3351 - loss: 1.8189 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3409 - loss: 1.7900
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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3452 - loss: 1.7679
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Epoch 74/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3457 - loss: 1.7302 
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3462 - loss: 1.7605 - val_accuracy: 0.3689 - val_loss: 1.6073
Epoch 75/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3518 - loss: 1.7803 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3457 - loss: 1.7808
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[1m160/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3449 - loss: 1.7770
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[1m239/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3451 - loss: 1.7719
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Epoch 76/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3586 - loss: 1.7432 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3580 - loss: 1.7504
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[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3560 - loss: 1.7475
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3545 - loss: 1.7478
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3529 - loss: 1.7495
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3509 - loss: 1.7538 - val_accuracy: 0.3658 - val_loss: 1.6087
Epoch 77/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4062 - loss: 1.5289
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3657 - loss: 1.6947 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3589 - loss: 1.7145
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[1m145/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3541 - loss: 1.7266
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3526 - loss: 1.7299
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3515 - loss: 1.7325
[1m262/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3506 - loss: 1.7345
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3500 - loss: 1.7359 - val_accuracy: 0.3702 - val_loss: 1.6117
Epoch 78/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 24ms/step - accuracy: 0.2969 - loss: 1.9560
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3319 - loss: 1.7588 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3372 - loss: 1.7539
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3384 - loss: 1.7513
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3389 - loss: 1.7495
[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3394 - loss: 1.7485
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3401 - loss: 1.7474
[1m262/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3406 - loss: 1.7471
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3410 - loss: 1.7470 - val_accuracy: 0.3747 - val_loss: 1.6176
Epoch 79/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3750 - loss: 1.8521
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3655 - loss: 1.7503 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3636 - loss: 1.7337
[1m119/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3610 - loss: 1.7326
[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3592 - loss: 1.7326
[1m197/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3574 - loss: 1.7329
[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3564 - loss: 1.7337
[1m276/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3555 - loss: 1.7342
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3552 - loss: 1.7346 - val_accuracy: 0.3735 - val_loss: 1.6061
Epoch 80/110

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Epoch 81/110

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Epoch 82/110

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Epoch 83/110

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Epoch 84/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3381 - loss: 1.7314 
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[1m245/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3511 - loss: 1.7291
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Epoch 85/110

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[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3519 - loss: 1.7399
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3519 - loss: 1.7366
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3518 - loss: 1.7343
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3515 - loss: 1.7336 - val_accuracy: 0.3626 - val_loss: 1.6030
Epoch 86/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3750 - loss: 1.7561
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3500 - loss: 1.7607 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3504 - loss: 1.7542
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3500 - loss: 1.7511
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3509 - loss: 1.7473
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3517 - loss: 1.7434
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3517 - loss: 1.7414
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3516 - loss: 1.7401
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3516 - loss: 1.7392 - val_accuracy: 0.3691 - val_loss: 1.5966
Epoch 87/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3438 - loss: 1.8064
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3437 - loss: 1.7210 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3494 - loss: 1.7235
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3515 - loss: 1.7280
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3518 - loss: 1.7301
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3512 - loss: 1.7324
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3509 - loss: 1.7332
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3509 - loss: 1.7335
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3509 - loss: 1.7335 - val_accuracy: 0.3745 - val_loss: 1.6027

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:13[0m 868ms/step
[1m68/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 754us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 9ms/step
Saved model to disk.

Accuracy capturado en la ejecución 1: 37.6 [%]
F1-score capturado en la ejecución 1: 36.87 [%]

=== EJECUCIÓN 2 ===

--- TRAIN (ejecución 2) ---

--- TEST (ejecución 2) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18676, 6, 250)

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:26[0m 869ms/step
[1m 60/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 860us/step  
[1m128/584[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 796us/step
[1m205/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 742us/step
[1m278/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 727us/step
[1m349/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 723us/step
[1m418/584[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 723us/step
[1m488/584[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 722us/step
[1m559/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 721us/step
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m63/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 815us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step
[1m 68/169[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 754us/step
[1m134/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 761us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 37.45 [%]
Global F1 score (validation) = 36.42 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[2.2964022e-01 2.0515576e-01 2.1003360e-01 ... 2.3384259e-06
  1.3045524e-01 9.3677025e-03]
 [2.2488698e-01 2.0828637e-01 2.1059375e-01 ... 3.9506567e-06
  1.3154635e-01 7.2478964e-03]
 [2.0357917e-01 1.8738268e-01 1.9895981e-01 ... 6.5502296e-05
  1.6764638e-01 3.5322133e-02]
 ...
 [2.0205913e-01 2.0772034e-01 2.0341410e-01 ... 1.6572631e-04
  1.5174188e-01 1.1729737e-02]
 [1.9079579e-01 2.1105939e-01 2.0613481e-01 ... 5.9420058e-06
  1.8551576e-01 4.2528249e-03]
 [1.1414883e-01 1.4839247e-01 1.2707527e-01 ... 7.5100525e-04
  1.1156704e-01 2.0892669e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 43.51 [%]
Global accuracy score (test) = 37.38 [%]
Global F1 score (train) = 41.25 [%]
Global F1 score (test) = 35.01 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.25      0.59      0.35       184
 CAMINAR CON MÓVIL O LIBRO       0.22      0.40      0.29       184
       CAMINAR USUAL SPEED       0.19      0.03      0.05       184
            CAMINAR ZIGZAG       0.00      0.00      0.00       184
          DE PIE BARRIENDO       0.26      0.53      0.35       184
   DE PIE DOBLANDO TOALLAS       0.33      0.11      0.16       184
    DE PIE MOVIENDO LIBROS       0.28      0.33      0.30       184
          DE PIE USANDO PC       0.39      0.60      0.47       184
        FASE REPOSO CON K5       0.68      0.83      0.75       184
INCREMENTAL CICLOERGOMETRO       0.92      0.61      0.73       184
           SENTADO LEYENDO       0.33      0.37      0.35       184
         SENTADO USANDO PC       0.20      0.09      0.12       184
      SENTADO VIENDO LA TV       0.32      0.26      0.29       184
   SUBIR Y BAJAR ESCALERAS       0.33      0.17      0.22       184
                    TROTAR       0.91      0.73      0.81       161

                  accuracy                           0.37      2737
                 macro avg       0.37      0.38      0.35      2737
              weighted avg       0.37      0.37      0.35      2737

2025-10-28 14:03:13.079568: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 14:03:13.091060: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1761656593.104584 2217604 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761656593.108694 2217604 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761656593.118704 2217604 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761656593.118732 2217604 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761656593.118735 2217604 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761656593.118736 2217604 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 14:03:13.121878: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1761656595.484901 2217604 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/110
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761656597.215329 2217713 service.cc:152] XLA service 0x7b99d800b9d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761656597.215376 2217713 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 14:03:17.252531: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
I0000 00:00:1761656597.418439 2217713 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761656599.696536 2217713 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/110

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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0949 - loss: 3.2043
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Epoch 3/110

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Epoch 4/110

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[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1164 - loss: 2.9399
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Epoch 5/110

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Epoch 6/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1500 - loss: 2.7500 
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1415 - loss: 2.7413 - val_accuracy: 0.1993 - val_loss: 2.3045
Epoch 7/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1376 - loss: 2.7180 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1397 - loss: 2.7083
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[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1433 - loss: 2.6976
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1439 - loss: 2.6945
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1443 - loss: 2.6907
[1m263/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1449 - loss: 2.6871
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1453 - loss: 2.6843 - val_accuracy: 0.2117 - val_loss: 2.2703
Epoch 8/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 24ms/step - accuracy: 0.1094 - loss: 2.6101
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1485 - loss: 2.6084 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1507 - loss: 2.6097
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1510 - loss: 2.6100
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1514 - loss: 2.6090
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1518 - loss: 2.6081
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1519 - loss: 2.6073
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1520 - loss: 2.6059
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1522 - loss: 2.6048 - val_accuracy: 0.2176 - val_loss: 2.2319
Epoch 9/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1094 - loss: 2.7045
[1m 34/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1669 - loss: 2.5534 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1686 - loss: 2.5485
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[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1674 - loss: 2.5414
[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1671 - loss: 2.5395
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1667 - loss: 2.5390
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Epoch 10/110

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[1m 42/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1705 - loss: 2.4924 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1719 - loss: 2.4893
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[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1740 - loss: 2.4853
[1m197/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1752 - loss: 2.4834
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1759 - loss: 2.4815
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1766 - loss: 2.4792 - val_accuracy: 0.2655 - val_loss: 2.1339
Epoch 11/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1406 - loss: 2.5242
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1753 - loss: 2.4386 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1764 - loss: 2.4412
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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1794 - loss: 2.4318
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1794 - loss: 2.4307
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1793 - loss: 2.4303 - val_accuracy: 0.2477 - val_loss: 2.1045
Epoch 12/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 26ms/step - accuracy: 0.2031 - loss: 2.4067
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1901 - loss: 2.3878 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1837 - loss: 2.3934
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[1m147/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1822 - loss: 2.3918
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1826 - loss: 2.3894
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1830 - loss: 2.3882
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1834 - loss: 2.3871
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1838 - loss: 2.3863 - val_accuracy: 0.2649 - val_loss: 2.0688
Epoch 13/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1562 - loss: 2.3442
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1913 - loss: 2.3743 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1918 - loss: 2.3714
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1912 - loss: 2.3702
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1921 - loss: 2.3679
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1929 - loss: 2.3650
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1936 - loss: 2.3618
[1m263/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1937 - loss: 2.3599
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1938 - loss: 2.3584 - val_accuracy: 0.2579 - val_loss: 2.0500
Epoch 14/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 25ms/step - accuracy: 0.2500 - loss: 2.1887
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1999 - loss: 2.3111 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2057 - loss: 2.3002
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2063 - loss: 2.3009
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2060 - loss: 2.3018
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2059 - loss: 2.3018
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2056 - loss: 2.3021
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2054 - loss: 2.3020
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2053 - loss: 2.3021 - val_accuracy: 0.2537 - val_loss: 2.0072
Epoch 15/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1562 - loss: 2.3449
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1966 - loss: 2.2943 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2023 - loss: 2.2858
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2029 - loss: 2.2845
[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2031 - loss: 2.2829
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2032 - loss: 2.2805
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2034 - loss: 2.2786
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2036 - loss: 2.2773
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Epoch 16/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2228 - loss: 2.2522 
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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2169 - loss: 2.2485
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Epoch 17/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 26ms/step - accuracy: 0.2188 - loss: 2.1680
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2244 - loss: 2.2094 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2222 - loss: 2.2100
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[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2184 - loss: 2.2100
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2177 - loss: 2.2099 - val_accuracy: 0.2920 - val_loss: 1.9281
Epoch 18/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2500 - loss: 2.1398
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2231 - loss: 2.2012 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2177 - loss: 2.1993
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[1m147/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2162 - loss: 2.1975
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2163 - loss: 2.1964
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2162 - loss: 2.1958
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2161 - loss: 2.1949
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2164 - loss: 2.1938 - val_accuracy: 0.2703 - val_loss: 1.9225
Epoch 19/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.1094 - loss: 2.2633
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2048 - loss: 2.1824 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2136 - loss: 2.1749
[1m111/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2176 - loss: 2.1742
[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2191 - loss: 2.1748
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2198 - loss: 2.1747
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2207 - loss: 2.1734
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2214 - loss: 2.1719
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2217 - loss: 2.1713 - val_accuracy: 0.3027 - val_loss: 1.8943
Epoch 20/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2969 - loss: 2.0221
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2455 - loss: 2.1116 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2466 - loss: 2.1166
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2452 - loss: 2.1213
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2428 - loss: 2.1261
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2415 - loss: 2.1295
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2409 - loss: 2.1309
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2403 - loss: 2.1319
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Epoch 21/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1875 - loss: 2.2665
[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2323 - loss: 2.1524 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2361 - loss: 2.1376
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[1m162/292[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2373 - loss: 2.1312
[1m203/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2375 - loss: 2.1294
[1m245/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2375 - loss: 2.1282
[1m280/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2376 - loss: 2.1269
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2375 - loss: 2.1265 - val_accuracy: 0.3134 - val_loss: 1.8653
Epoch 22/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1562 - loss: 2.0969
[1m 33/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2290 - loss: 2.1480 
[1m 70/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2329 - loss: 2.1330
[1m109/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2359 - loss: 2.1247
[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2370 - loss: 2.1219
[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2368 - loss: 2.1210
[1m219/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2370 - loss: 2.1193
[1m257/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2373 - loss: 2.1176
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2373 - loss: 2.1161 - val_accuracy: 0.3123 - val_loss: 1.8551
Epoch 23/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 26ms/step - accuracy: 0.2812 - loss: 2.0139
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2406 - loss: 2.0882 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2404 - loss: 2.0910
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[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2415 - loss: 2.0923
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2422 - loss: 2.0914
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2427 - loss: 2.0908
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2434 - loss: 2.0903
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2437 - loss: 2.0899 - val_accuracy: 0.3142 - val_loss: 1.8493
Epoch 24/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1250 - loss: 2.2528
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2333 - loss: 2.0902 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2356 - loss: 2.0870
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2380 - loss: 2.0850
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2395 - loss: 2.0835
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2404 - loss: 2.0822
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2412 - loss: 2.0807
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2419 - loss: 2.0795
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2422 - loss: 2.0790 - val_accuracy: 0.3219 - val_loss: 1.8343
Epoch 25/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2031 - loss: 2.2781
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2492 - loss: 2.0818 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2530 - loss: 2.0671
[1m122/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2535 - loss: 2.0624
[1m162/292[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2535 - loss: 2.0614
[1m200/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2539 - loss: 2.0609
[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2544 - loss: 2.0607
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2547 - loss: 2.0605
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2548 - loss: 2.0604 - val_accuracy: 0.3136 - val_loss: 1.8334
Epoch 26/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1562 - loss: 2.1580
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2444 - loss: 2.0535 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2520 - loss: 2.0492
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2540 - loss: 2.0495
[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2549 - loss: 2.0489
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2552 - loss: 2.0491
[1m222/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2552 - loss: 2.0492
[1m262/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2551 - loss: 2.0494
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2551 - loss: 2.0495 - val_accuracy: 0.3182 - val_loss: 1.8234
Epoch 27/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.3281 - loss: 1.9765
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2773 - loss: 2.0286 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2697 - loss: 2.0362
[1m120/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2662 - loss: 2.0374
[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2643 - loss: 2.0371
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2628 - loss: 2.0368
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2615 - loss: 2.0364
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Epoch 28/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2500 - loss: 2.0471
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2766 - loss: 2.0344 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2677 - loss: 2.0416
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[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2645 - loss: 2.0359
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2643 - loss: 2.0338
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2645 - loss: 2.0315
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2645 - loss: 2.0303 - val_accuracy: 0.3179 - val_loss: 1.8025
Epoch 29/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2812 - loss: 1.9704
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2664 - loss: 2.0217 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2669 - loss: 2.0130
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[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2651 - loss: 2.0059
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2649 - loss: 2.0055
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2649 - loss: 2.0057
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2648 - loss: 2.0063
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2647 - loss: 2.0068 - val_accuracy: 0.3345 - val_loss: 1.7941
Epoch 30/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3750 - loss: 2.0515
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2750 - loss: 1.9912 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2708 - loss: 2.0013
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2685 - loss: 2.0028
[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2670 - loss: 2.0045
[1m200/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2660 - loss: 2.0062
[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2660 - loss: 2.0065
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2662 - loss: 2.0065
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2664 - loss: 2.0063 - val_accuracy: 0.3234 - val_loss: 1.7809
Epoch 31/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2500 - loss: 2.1234
[1m 34/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2666 - loss: 1.9994 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2733 - loss: 1.9943
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2737 - loss: 1.9953
[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2733 - loss: 1.9964
[1m200/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2730 - loss: 1.9971
[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2731 - loss: 1.9970
[1m278/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2731 - loss: 1.9969
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2731 - loss: 1.9968 - val_accuracy: 0.3349 - val_loss: 1.7741
Epoch 32/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2812 - loss: 1.8382
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2796 - loss: 1.9505 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2791 - loss: 1.9601
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2809 - loss: 1.9686
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2807 - loss: 1.9704
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Epoch 33/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2188 - loss: 2.2461
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2833 - loss: 1.9914 
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Epoch 34/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3281 - loss: 1.9156
[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2943 - loss: 1.9510 
[1m 83/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2889 - loss: 1.9532
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[1m243/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2814 - loss: 1.9658
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2815 - loss: 1.9662 - val_accuracy: 0.3375 - val_loss: 1.7554
Epoch 35/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1875 - loss: 2.1348
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2780 - loss: 1.9900 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2826 - loss: 1.9740
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[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2874 - loss: 1.9627
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2876 - loss: 1.9622
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2876 - loss: 1.9620
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2876 - loss: 1.9616 - val_accuracy: 0.3436 - val_loss: 1.7481
Epoch 36/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1562 - loss: 2.0432
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2748 - loss: 1.9304 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2787 - loss: 1.9340
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[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2789 - loss: 1.9420
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2794 - loss: 1.9452
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2798 - loss: 1.9471
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2803 - loss: 1.9477
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2806 - loss: 1.9482 - val_accuracy: 0.3380 - val_loss: 1.7409
Epoch 37/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2500 - loss: 1.9627
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2942 - loss: 1.8888 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2935 - loss: 1.9090
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[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2938 - loss: 1.9202
[1m199/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2944 - loss: 1.9230
[1m237/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2948 - loss: 1.9255
[1m276/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2951 - loss: 1.9273
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Epoch 38/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2837 - loss: 1.9553 
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Epoch 39/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2866 - loss: 1.9352 
[1m 68/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2905 - loss: 1.9310
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[1m256/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2962 - loss: 1.9219
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Epoch 40/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3220 - loss: 1.9085 
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[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3117 - loss: 1.9077
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3106 - loss: 1.9084
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3098 - loss: 1.9089
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3093 - loss: 1.9092 - val_accuracy: 0.3476 - val_loss: 1.7171
Epoch 41/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3097 - loss: 1.8910 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3121 - loss: 1.8903
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[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3085 - loss: 1.8961
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3073 - loss: 1.8985
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3067 - loss: 1.9000
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3064 - loss: 1.9004 - val_accuracy: 0.3525 - val_loss: 1.7111
Epoch 42/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2969 - loss: 1.8616
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3092 - loss: 1.9005 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3153 - loss: 1.8924
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[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3151 - loss: 1.8893
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3125 - loss: 1.8892
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Epoch 43/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3089 - loss: 1.8816 
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Epoch 44/110

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Epoch 45/110

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Epoch 46/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3216 - loss: 1.8831 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3206 - loss: 1.8804
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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3158 - loss: 1.8853
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3152 - loss: 1.8847
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3150 - loss: 1.8843 - val_accuracy: 0.3547 - val_loss: 1.6750
Epoch 47/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3060 - loss: 1.8588 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3096 - loss: 1.8604
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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3118 - loss: 1.8615
[1m275/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3117 - loss: 1.8623
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Epoch 48/110

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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3207 - loss: 1.8600 - val_accuracy: 0.3526 - val_loss: 1.6651
Epoch 49/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3227 - loss: 1.8911 
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Epoch 50/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3121 - loss: 1.8742 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3140 - loss: 1.8628
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3169 - loss: 1.8586
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Epoch 51/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3281 - loss: 1.8962
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3186 - loss: 1.8324 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3153 - loss: 1.8435
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[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3138 - loss: 1.8448
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3138 - loss: 1.8455
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3139 - loss: 1.8467
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3140 - loss: 1.8473 - val_accuracy: 0.3523 - val_loss: 1.6566
Epoch 52/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3125 - loss: 2.0336
[1m 31/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3486 - loss: 1.8176 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3414 - loss: 1.8221
[1m105/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3387 - loss: 1.8252
[1m144/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3365 - loss: 1.8284
[1m183/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3350 - loss: 1.8307
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3335 - loss: 1.8334
[1m262/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3323 - loss: 1.8351
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3315 - loss: 1.8360 - val_accuracy: 0.3512 - val_loss: 1.6526
Epoch 53/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.2969 - loss: 1.9600
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3146 - loss: 1.8814 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3214 - loss: 1.8557
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[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3223 - loss: 1.8481
[1m182/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3224 - loss: 1.8464
[1m217/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3227 - loss: 1.8452
[1m257/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3229 - loss: 1.8439
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Epoch 54/110

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Epoch 55/110

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Epoch 56/110

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Epoch 57/110

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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3264 - loss: 1.8218 - val_accuracy: 0.3523 - val_loss: 1.6371
Epoch 58/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3567 - loss: 1.7606 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3524 - loss: 1.7641
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[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3397 - loss: 1.7917
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Epoch 59/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3205 - loss: 1.8936 
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Epoch 60/110

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Epoch 61/110

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Epoch 62/110

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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3347 - loss: 1.8000 - val_accuracy: 0.3626 - val_loss: 1.6273
Epoch 63/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3434 - loss: 1.7992 
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3334 - loss: 1.8148
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3334 - loss: 1.8144
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Epoch 64/110

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Epoch 65/110

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Epoch 66/110

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Epoch 67/110

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Epoch 68/110

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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3299 - loss: 1.7964
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Epoch 69/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3304 - loss: 1.8003 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3274 - loss: 1.7958
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[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3304 - loss: 1.7844
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Epoch 70/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3218 - loss: 1.7845 
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Epoch 71/110

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Epoch 72/110

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Epoch 73/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3396 - loss: 1.7991 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3358 - loss: 1.7936
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3362 - loss: 1.7862
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3366 - loss: 1.7847
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3368 - loss: 1.7839 - val_accuracy: 0.3698 - val_loss: 1.6091
Epoch 74/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3330 - loss: 1.7556 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3373 - loss: 1.7580
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[1m160/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3401 - loss: 1.7598
[1m198/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3405 - loss: 1.7610
[1m237/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3407 - loss: 1.7623
[1m275/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3408 - loss: 1.7628
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3409 - loss: 1.7629 - val_accuracy: 0.3636 - val_loss: 1.6139
Epoch 75/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3458 - loss: 1.7415 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3463 - loss: 1.7395
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Epoch 76/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3378 - loss: 1.7194 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3410 - loss: 1.7312
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Epoch 77/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3547 - loss: 1.7269 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3525 - loss: 1.7333
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3518 - loss: 1.7421
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Epoch 78/110

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[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3546 - loss: 1.7432
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[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3504 - loss: 1.7460
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3491 - loss: 1.7480
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3479 - loss: 1.7499 - val_accuracy: 0.3715 - val_loss: 1.6059
Epoch 79/110

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[1m 34/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3357 - loss: 1.7690 
[1m 70/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3421 - loss: 1.7506
[1m109/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3444 - loss: 1.7477
[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3456 - loss: 1.7465
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3451 - loss: 1.7479
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3448 - loss: 1.7495
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3446 - loss: 1.7506
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3445 - loss: 1.7510 - val_accuracy: 0.3661 - val_loss: 1.6057
Epoch 80/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3351 - loss: 1.7369 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3400 - loss: 1.7379
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[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3432 - loss: 1.7388
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3447 - loss: 1.7390
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3456 - loss: 1.7395
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3462 - loss: 1.7399
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3464 - loss: 1.7400 - val_accuracy: 0.3678 - val_loss: 1.6066
Epoch 81/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3588 - loss: 1.7653 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3545 - loss: 1.7593
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Epoch 82/110

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Epoch 83/110

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Epoch 84/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3655 - loss: 1.7617 
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3518 - loss: 1.7383
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3507 - loss: 1.7371 - val_accuracy: 0.3748 - val_loss: 1.6016
Epoch 85/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3752 - loss: 1.7325 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3732 - loss: 1.7216
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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3633 - loss: 1.7253
[1m263/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3619 - loss: 1.7265
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Epoch 86/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3545 - loss: 1.7581 
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[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3497 - loss: 1.7366
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Epoch 87/110

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[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3434 - loss: 1.7615
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Epoch 88/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3394 - loss: 1.7299 
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[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3422 - loss: 1.7401
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Epoch 89/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3550 - loss: 1.6887 
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[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3500 - loss: 1.7150
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3503 - loss: 1.7182 - val_accuracy: 0.3673 - val_loss: 1.6092
Epoch 90/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3755 - loss: 1.7507 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3714 - loss: 1.7382
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3678 - loss: 1.7357
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3659 - loss: 1.7322
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3644 - loss: 1.7302
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3633 - loss: 1.7287
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3621 - loss: 1.7282
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3613 - loss: 1.7278 - val_accuracy: 0.3643 - val_loss: 1.5978
Epoch 91/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3495 - loss: 1.6543 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3482 - loss: 1.6716
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[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3474 - loss: 1.6887
[1m198/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3476 - loss: 1.6942
[1m237/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3482 - loss: 1.6978
[1m277/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3489 - loss: 1.7007
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Epoch 92/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3455 - loss: 1.6760 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3480 - loss: 1.6909
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[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3519 - loss: 1.7081
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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:13[0m 867ms/step
[1m59/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 862us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
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Saved model to disk.

Accuracy capturado en la ejecución 2: 37.38 [%]
F1-score capturado en la ejecución 2: 35.01 [%]

=== EJECUCIÓN 3 ===

--- TRAIN (ejecución 3) ---

--- TEST (ejecución 3) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18676, 6, 250)

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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m63/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 814us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 68/169[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 755us/step
[1m140/169[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 728us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 36.65 [%]
Global F1 score (validation) = 36.28 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[2.0201564e-01 2.1119766e-01 2.1524201e-01 ... 1.2745146e-06
  1.5370893e-01 5.6210365e-03]
 [1.9806431e-01 2.0358670e-01 2.1238491e-01 ... 1.2469720e-05
  1.5319782e-01 8.0957776e-03]
 [1.9716910e-01 2.0549656e-01 2.1220268e-01 ... 8.1885873e-06
  1.6285816e-01 9.5894113e-03]
 ...
 [1.9179809e-01 2.1301095e-01 2.0818344e-01 ... 2.8117978e-05
  1.5963621e-01 8.1302039e-03]
 [1.8543433e-01 2.1013574e-01 1.9444215e-01 ... 5.2858727e-06
  1.9304714e-01 2.1624692e-02]
 [7.2910257e-02 1.0011200e-01 7.6498441e-02 ... 3.1217156e-04
  9.8739408e-02 2.1856655e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 43.29 [%]
Global accuracy score (test) = 37.01 [%]
Global F1 score (train) = 42.34 [%]
Global F1 score (test) = 36.23 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.00      0.00      0.00       184
 CAMINAR CON MÓVIL O LIBRO       0.20      0.35      0.26       184
       CAMINAR USUAL SPEED       0.25      0.27      0.26       184
            CAMINAR ZIGZAG       0.15      0.24      0.19       184
          DE PIE BARRIENDO       0.20      0.41      0.27       184
   DE PIE DOBLANDO TOALLAS       0.32      0.14      0.20       184
    DE PIE MOVIENDO LIBROS       0.29      0.33      0.31       184
          DE PIE USANDO PC       0.43      0.60      0.50       184
        FASE REPOSO CON K5       0.70      0.86      0.77       184
INCREMENTAL CICLOERGOMETRO       0.86      0.61      0.71       184
           SENTADO LEYENDO       0.34      0.37      0.35       184
         SENTADO USANDO PC       0.36      0.11      0.17       184
      SENTADO VIENDO LA TV       0.41      0.42      0.42       184
   SUBIR Y BAJAR ESCALERAS       0.38      0.15      0.21       184
                    TROTAR       0.93      0.73      0.82       161

                  accuracy                           0.37      2737
                 macro avg       0.39      0.37      0.36      2737
              weighted avg       0.38      0.37      0.36      2737

2025-10-28 14:04:27.290937: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 14:04:27.302133: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1761656667.315266 2227229 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761656667.319489 2227229 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761656667.329430 2227229 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761656667.329450 2227229 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761656667.329461 2227229 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761656667.329463 2227229 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 14:04:27.332708: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1761656669.701945 2227229 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/110
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761656671.418547 2227358 service.cc:152] XLA service 0x7972d8003b70 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761656671.418616 2227358 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 14:04:31.457656: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
I0000 00:00:1761656671.623732 2227358 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761656673.925766 2227358 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16:54[0m 3s/step - accuracy: 0.0469 - loss: 3.9326
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Epoch 2/110

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Epoch 3/110

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Epoch 4/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1289 - loss: 2.9097 
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[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1272 - loss: 2.8817
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1267 - loss: 2.8753 - val_accuracy: 0.1872 - val_loss: 2.3469
Epoch 5/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1453 - loss: 2.7520 
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[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1430 - loss: 2.7538
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Epoch 6/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1427 - loss: 2.6629 
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Epoch 7/110

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Epoch 8/110

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[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1615 - loss: 2.5664
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1625 - loss: 2.5623 - val_accuracy: 0.2329 - val_loss: 2.1964
Epoch 9/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1406 - loss: 2.6060
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1624 - loss: 2.5007 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1674 - loss: 2.4978
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[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1690 - loss: 2.4995
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1692 - loss: 2.4991
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1692 - loss: 2.4982
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1694 - loss: 2.4968
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1695 - loss: 2.4958 - val_accuracy: 0.2207 - val_loss: 2.1553
Epoch 10/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2656 - loss: 2.2556
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1845 - loss: 2.4195 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1819 - loss: 2.4316
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1796 - loss: 2.4388
[1m145/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1780 - loss: 2.4412
[1m183/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1767 - loss: 2.4426
[1m218/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1762 - loss: 2.4427
[1m251/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1761 - loss: 2.4422
[1m289/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1763 - loss: 2.4414
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1763 - loss: 2.4413 - val_accuracy: 0.2307 - val_loss: 2.1253
Epoch 11/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1562 - loss: 2.4569
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1692 - loss: 2.4460 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1725 - loss: 2.4356
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[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1769 - loss: 2.4223
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1785 - loss: 2.4189
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1797 - loss: 2.4162
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1805 - loss: 2.4140
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Epoch 12/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1809 - loss: 2.3994 
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Epoch 13/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1937 - loss: 2.3415 
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1964 - loss: 2.3254
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1964 - loss: 2.3236 - val_accuracy: 0.2348 - val_loss: 2.0305
Epoch 14/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.1719 - loss: 2.2594
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2038 - loss: 2.2795 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2024 - loss: 2.2855
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2012 - loss: 2.2885
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2011 - loss: 2.2896
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2013 - loss: 2.2894
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2015 - loss: 2.2887
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2018 - loss: 2.2878
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2020 - loss: 2.2873 - val_accuracy: 0.2481 - val_loss: 1.9965
Epoch 15/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1875 - loss: 2.3603
[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2022 - loss: 2.2893 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2018 - loss: 2.2833
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2026 - loss: 2.2790
[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2023 - loss: 2.2772
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2021 - loss: 2.2753
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2019 - loss: 2.2732
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2020 - loss: 2.2711
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2021 - loss: 2.2700 - val_accuracy: 0.2357 - val_loss: 1.9760
Epoch 16/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2812 - loss: 2.1332
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2162 - loss: 2.2241 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2141 - loss: 2.2256
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2150 - loss: 2.2242
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2150 - loss: 2.2226
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2147 - loss: 2.2214
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2145 - loss: 2.2202
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2141 - loss: 2.2198
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Epoch 17/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 27ms/step - accuracy: 0.2188 - loss: 2.0818
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2078 - loss: 2.2037 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2090 - loss: 2.1996
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[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2112 - loss: 2.1953
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2123 - loss: 2.1938
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2135 - loss: 2.1925
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2143 - loss: 2.1917
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2148 - loss: 2.1912 - val_accuracy: 0.2548 - val_loss: 1.9412
Epoch 18/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1719 - loss: 2.1272
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2378 - loss: 2.1507 
[1m 70/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2366 - loss: 2.1588
[1m108/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2340 - loss: 2.1645
[1m146/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2325 - loss: 2.1673
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2318 - loss: 2.1678
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2308 - loss: 2.1681
[1m256/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2304 - loss: 2.1680
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2300 - loss: 2.1680 - val_accuracy: 0.2707 - val_loss: 1.9099
Epoch 19/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1406 - loss: 2.4632
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2135 - loss: 2.2036 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2157 - loss: 2.1958
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[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2187 - loss: 2.1818
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2198 - loss: 2.1774
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2205 - loss: 2.1737
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2211 - loss: 2.1705
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2214 - loss: 2.1690 - val_accuracy: 0.2799 - val_loss: 1.8892
Epoch 20/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.2188 - loss: 2.0870
[1m 32/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2553 - loss: 2.1279 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2484 - loss: 2.1278
[1m111/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2435 - loss: 2.1308
[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2409 - loss: 2.1321
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2396 - loss: 2.1324
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2386 - loss: 2.1324
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2378 - loss: 2.1322
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2374 - loss: 2.1319 - val_accuracy: 0.2792 - val_loss: 1.8847
Epoch 21/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2656 - loss: 1.8881
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2310 - loss: 2.0874 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2316 - loss: 2.0910
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2331 - loss: 2.0948
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2346 - loss: 2.0993
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2357 - loss: 2.1023
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2362 - loss: 2.1048
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2365 - loss: 2.1062
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2367 - loss: 2.1068 - val_accuracy: 0.2799 - val_loss: 1.8673
Epoch 22/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2656 - loss: 2.2193
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2242 - loss: 2.1152 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2233 - loss: 2.1059
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2239 - loss: 2.1056
[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2250 - loss: 2.1066
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2262 - loss: 2.1073
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2274 - loss: 2.1075
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2283 - loss: 2.1072
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2292 - loss: 2.1066 - val_accuracy: 0.2772 - val_loss: 1.8532
Epoch 23/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2500 - loss: 2.0179
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2549 - loss: 2.0610 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2444 - loss: 2.0723
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[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2432 - loss: 2.0780
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Epoch 24/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2500 - loss: 2.0502
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2588 - loss: 2.0662 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2561 - loss: 2.0621
[1m119/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2552 - loss: 2.0614
[1m161/292[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2550 - loss: 2.0610
[1m198/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2544 - loss: 2.0612
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2538 - loss: 2.0612
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2534 - loss: 2.0613
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2532 - loss: 2.0615 - val_accuracy: 0.3029 - val_loss: 1.8351
Epoch 25/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2656 - loss: 2.0358
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2588 - loss: 2.0406 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2596 - loss: 2.0420
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2596 - loss: 2.0428
[1m147/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2590 - loss: 2.0450
[1m182/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2585 - loss: 2.0460
[1m219/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2580 - loss: 2.0468
[1m253/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2576 - loss: 2.0476
[1m290/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2572 - loss: 2.0481
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2572 - loss: 2.0481 - val_accuracy: 0.3077 - val_loss: 1.8217
Epoch 26/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2344 - loss: 2.2412
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2634 - loss: 2.0590 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2615 - loss: 2.0496
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2590 - loss: 2.0482
[1m147/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2579 - loss: 2.0466
[1m183/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2574 - loss: 2.0449
[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2567 - loss: 2.0440
[1m259/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2560 - loss: 2.0438
[1m290/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2555 - loss: 2.0439
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2554 - loss: 2.0439 - val_accuracy: 0.3130 - val_loss: 1.8187
Epoch 27/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2812 - loss: 1.8547
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2709 - loss: 1.9974 
[1m 70/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2662 - loss: 2.0120
[1m106/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2655 - loss: 2.0169
[1m146/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2644 - loss: 2.0222
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2637 - loss: 2.0246
[1m222/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2634 - loss: 2.0251
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2634 - loss: 2.0255
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2633 - loss: 2.0258 - val_accuracy: 0.3088 - val_loss: 1.8028
Epoch 28/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2812 - loss: 2.0424
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2642 - loss: 2.0277 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2670 - loss: 2.0205
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2678 - loss: 2.0196
[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2670 - loss: 2.0208
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2665 - loss: 2.0203
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2665 - loss: 2.0197
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2663 - loss: 2.0192
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2662 - loss: 2.0190 - val_accuracy: 0.3016 - val_loss: 1.8108
Epoch 29/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2500 - loss: 2.1239
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2558 - loss: 2.0137 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2597 - loss: 2.0066
[1m109/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2614 - loss: 2.0086
[1m147/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2624 - loss: 2.0073
[1m181/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2631 - loss: 2.0060
[1m218/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2637 - loss: 2.0051
[1m253/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2641 - loss: 2.0048
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.2644 - loss: 2.0047
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2644 - loss: 2.0047 - val_accuracy: 0.3171 - val_loss: 1.7871
Epoch 30/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2656 - loss: 2.0401
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2639 - loss: 2.0157 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2727 - loss: 2.0037
[1m108/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2741 - loss: 2.0008
[1m142/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2741 - loss: 2.0000
[1m181/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2742 - loss: 1.9996
[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2741 - loss: 1.9997
[1m253/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2741 - loss: 1.9997
[1m289/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2740 - loss: 1.9999
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2740 - loss: 1.9999 - val_accuracy: 0.3138 - val_loss: 1.7837
Epoch 31/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.2500 - loss: 1.7711
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2682 - loss: 1.9574 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2684 - loss: 1.9711
[1m119/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2695 - loss: 1.9766
[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2699 - loss: 1.9800
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2705 - loss: 1.9827
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2711 - loss: 1.9840
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2714 - loss: 1.9845
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2716 - loss: 1.9847 - val_accuracy: 0.3175 - val_loss: 1.7784
Epoch 32/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2188 - loss: 1.8855
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2500 - loss: 1.9994 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2612 - loss: 1.9927
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2657 - loss: 1.9881
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2686 - loss: 1.9852
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2703 - loss: 1.9834
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2713 - loss: 1.9826
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2719 - loss: 1.9820
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2721 - loss: 1.9818 - val_accuracy: 0.3175 - val_loss: 1.7606
Epoch 33/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1719 - loss: 2.1045
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2718 - loss: 1.9959 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2816 - loss: 1.9835
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2833 - loss: 1.9757
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2830 - loss: 1.9700 - val_accuracy: 0.3230 - val_loss: 1.7518
Epoch 34/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2500 - loss: 2.0009
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2759 - loss: 1.9930 
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2808 - loss: 1.9690
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Epoch 35/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.4062 - loss: 2.0037
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3054 - loss: 1.9196 
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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2924 - loss: 1.9326
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2911 - loss: 1.9354 - val_accuracy: 0.3315 - val_loss: 1.7391
Epoch 36/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.2344 - loss: 2.0238
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2949 - loss: 1.9419 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2936 - loss: 1.9456
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[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2939 - loss: 1.9485
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2939 - loss: 1.9485
[1m222/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2940 - loss: 1.9477
[1m257/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2939 - loss: 1.9469
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2939 - loss: 1.9462 - val_accuracy: 0.3286 - val_loss: 1.7328
Epoch 37/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3281 - loss: 1.9439
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2948 - loss: 1.9419 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2936 - loss: 1.9441
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[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2930 - loss: 1.9429
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2929 - loss: 1.9417
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2926 - loss: 1.9412
[1m262/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2922 - loss: 1.9411
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2921 - loss: 1.9407 - val_accuracy: 0.3288 - val_loss: 1.7303
Epoch 38/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3125 - loss: 1.9726
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2927 - loss: 1.9118 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2967 - loss: 1.9089
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[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3002 - loss: 1.9093
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3001 - loss: 1.9118
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2995 - loss: 1.9136
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2990 - loss: 1.9152
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Epoch 39/110

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Epoch 40/110

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Epoch 41/110

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Epoch 42/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2950 - loss: 1.9091 
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Epoch 43/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2981 - loss: 1.9197 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2979 - loss: 1.9117
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Epoch 44/110

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Epoch 45/110

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Epoch 46/110

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Epoch 47/110

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[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3202 - loss: 1.8711
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3194 - loss: 1.8688 - val_accuracy: 0.3545 - val_loss: 1.6704
Epoch 48/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3262 - loss: 1.8178 
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Epoch 49/110

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Epoch 50/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2870 - loss: 1.8729 
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Epoch 51/110

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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3203 - loss: 1.8468
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3203 - loss: 1.8461
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3204 - loss: 1.8455 - val_accuracy: 0.3606 - val_loss: 1.6564
Epoch 52/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3470 - loss: 1.8108 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3378 - loss: 1.8200
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[1m243/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3297 - loss: 1.8317
[1m281/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3287 - loss: 1.8324
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3284 - loss: 1.8326 - val_accuracy: 0.3595 - val_loss: 1.6654
Epoch 53/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3125 - loss: 1.7670
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3276 - loss: 1.8300 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3284 - loss: 1.8310
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[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3287 - loss: 1.8264
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3283 - loss: 1.8274
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3279 - loss: 1.8289
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3275 - loss: 1.8295
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3274 - loss: 1.8297 - val_accuracy: 0.3574 - val_loss: 1.6624
Epoch 54/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3438 - loss: 1.8059
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3253 - loss: 1.8582 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3251 - loss: 1.8425
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3276 - loss: 1.8276
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3275 - loss: 1.8276
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Epoch 55/110

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Epoch 56/110

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Epoch 57/110

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Epoch 58/110

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[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3261 - loss: 1.7946
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Epoch 59/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3380 - loss: 1.8014 
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3306 - loss: 1.8076
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Epoch 60/110

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Epoch 61/110

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Epoch 62/110

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Epoch 63/110

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Epoch 64/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3378 - loss: 1.7937 
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Epoch 65/110

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Epoch 66/110

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Epoch 67/110

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Epoch 68/110

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Epoch 69/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3472 - loss: 1.7387 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3410 - loss: 1.7456
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[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3369 - loss: 1.7593
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Epoch 70/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3163 - loss: 1.8211 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3199 - loss: 1.8082
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3305 - loss: 1.7772
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Epoch 71/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3380 - loss: 1.7788 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3416 - loss: 1.7707
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3396 - loss: 1.7638
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3393 - loss: 1.7643 - val_accuracy: 0.3776 - val_loss: 1.6186
Epoch 72/110

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[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3484 - loss: 1.7449
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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3487 - loss: 1.7551
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Epoch 73/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3586 - loss: 1.6971 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3536 - loss: 1.7311
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3505 - loss: 1.7453
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[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3477 - loss: 1.7525
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3466 - loss: 1.7543
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3460 - loss: 1.7553
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3459 - loss: 1.7557 - val_accuracy: 0.3687 - val_loss: 1.6171
Epoch 74/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3750 - loss: 1.6523
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3741 - loss: 1.7182 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3649 - loss: 1.7315
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[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3596 - loss: 1.7429
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3586 - loss: 1.7454
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3574 - loss: 1.7473
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3562 - loss: 1.7490
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3558 - loss: 1.7498 - val_accuracy: 0.3795 - val_loss: 1.6091
Epoch 75/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.2656 - loss: 1.7734
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3565 - loss: 1.7398 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3595 - loss: 1.7401
[1m120/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3588 - loss: 1.7378
[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3583 - loss: 1.7381
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3576 - loss: 1.7396
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3568 - loss: 1.7409
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3559 - loss: 1.7422
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3555 - loss: 1.7430 - val_accuracy: 0.3726 - val_loss: 1.6120
Epoch 76/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.4062 - loss: 1.6101
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3555 - loss: 1.7387 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3492 - loss: 1.7473
[1m119/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3446 - loss: 1.7538
[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3428 - loss: 1.7568
[1m200/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3418 - loss: 1.7584
[1m239/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3412 - loss: 1.7587
[1m279/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3412 - loss: 1.7584
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Epoch 77/110

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Epoch 78/110

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Epoch 79/110

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[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3392 - loss: 1.7530
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3401 - loss: 1.7520
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3408 - loss: 1.7516 - val_accuracy: 0.3819 - val_loss: 1.6020
Epoch 80/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3535 - loss: 1.7369 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3542 - loss: 1.7399
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[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3504 - loss: 1.7356
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3498 - loss: 1.7350
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3492 - loss: 1.7350
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Epoch 81/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3700 - loss: 1.7036 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3638 - loss: 1.7072
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3569 - loss: 1.7238
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Epoch 82/110

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[1m 34/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3760 - loss: 1.7457 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3735 - loss: 1.7388
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3694 - loss: 1.7415
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3675 - loss: 1.7440
[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3665 - loss: 1.7445
[1m220/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3651 - loss: 1.7449
[1m254/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3642 - loss: 1.7446
[1m288/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3634 - loss: 1.7438
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3633 - loss: 1.7436 - val_accuracy: 0.3728 - val_loss: 1.6032
Epoch 83/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3594 - loss: 1.7050
[1m 34/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3697 - loss: 1.7156 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3668 - loss: 1.7182
[1m106/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3657 - loss: 1.7164
[1m146/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3620 - loss: 1.7177
[1m180/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3597 - loss: 1.7194
[1m218/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3579 - loss: 1.7218
[1m257/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3568 - loss: 1.7233
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3564 - loss: 1.7240 - val_accuracy: 0.3837 - val_loss: 1.6031
Epoch 84/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3750 - loss: 1.8178
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3588 - loss: 1.7276 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3605 - loss: 1.7238
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3600 - loss: 1.7228
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3604 - loss: 1.7232
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3603 - loss: 1.7243
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3600 - loss: 1.7253
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3598 - loss: 1.7266
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3596 - loss: 1.7273 - val_accuracy: 0.3726 - val_loss: 1.6061

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:12[0m 854ms/step
[1m68/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 748us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 9ms/step
Saved model to disk.

Accuracy capturado en la ejecución 3: 37.01 [%]
F1-score capturado en la ejecución 3: 36.23 [%]

=== EJECUCIÓN 4 ===

--- TRAIN (ejecución 4) ---

--- TEST (ejecución 4) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18676, 6, 250)

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:34[0m 883ms/step
[1m 64/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 802us/step  
[1m132/584[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 770us/step
[1m203/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 747us/step
[1m277/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 730us/step
[1m340/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 743us/step
[1m403/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 752us/step
[1m469/584[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 754us/step
[1m546/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 740us/step
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m62/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 829us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 8ms/step

[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 65/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 790us/step
[1m137/169[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 743us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 37.26 [%]
Global F1 score (validation) = 37.36 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[2.1073250e-01 1.9317092e-01 2.0698962e-01 ... 1.3941398e-06
  1.7347737e-01 9.1311373e-03]
 [2.0634896e-01 2.0811817e-01 2.1109134e-01 ... 3.5461269e-06
  1.6306353e-01 5.4187556e-03]
 [2.1182849e-01 1.8566675e-01 2.0993312e-01 ... 2.0915672e-06
  1.5907663e-01 2.9303148e-02]
 ...
 [2.0653704e-01 2.0958261e-01 2.0626561e-01 ... 5.3518583e-05
  1.5021829e-01 1.3671126e-02]
 [1.8798932e-01 2.0833978e-01 1.9629121e-01 ... 1.2565886e-04
  1.6367467e-01 6.1404826e-03]
 [5.9579805e-02 7.7989966e-02 6.6119283e-02 ... 6.7073740e-03
  9.5092230e-02 8.6902464e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 45.03 [%]
Global accuracy score (test) = 36.32 [%]
Global F1 score (train) = 44.7 [%]
Global F1 score (test) = 36.2 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.26      0.43      0.32       184
 CAMINAR CON MÓVIL O LIBRO       0.19      0.27      0.22       184
       CAMINAR USUAL SPEED       0.23      0.14      0.17       184
            CAMINAR ZIGZAG       0.18      0.11      0.14       184
          DE PIE BARRIENDO       0.22      0.41      0.28       184
   DE PIE DOBLANDO TOALLAS       0.30      0.11      0.16       184
    DE PIE MOVIENDO LIBROS       0.29      0.40      0.34       184
          DE PIE USANDO PC       0.41      0.55      0.47       184
        FASE REPOSO CON K5       0.70      0.77      0.74       184
INCREMENTAL CICLOERGOMETRO       0.93      0.60      0.73       184
           SENTADO LEYENDO       0.35      0.30      0.32       184
         SENTADO USANDO PC       0.18      0.14      0.16       184
      SENTADO VIENDO LA TV       0.38      0.40      0.39       184
   SUBIR Y BAJAR ESCALERAS       0.35      0.16      0.22       184
                    TROTAR       0.87      0.70      0.77       161

                  accuracy                           0.36      2737
                 macro avg       0.39      0.37      0.36      2737
              weighted avg       0.38      0.36      0.36      2737

2025-10-28 14:05:37.508870: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 14:05:37.520091: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1761656737.533198 2236088 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761656737.537450 2236088 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761656737.547334 2236088 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761656737.547354 2236088 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761656737.547358 2236088 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761656737.547360 2236088 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 14:05:37.550701: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1761656739.930987 2236088 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/110
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761656741.642326 2236214 service.cc:152] XLA service 0x7a3f3000c0f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761656741.642389 2236214 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 14:05:41.677709: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
I0000 00:00:1761656741.848738 2236214 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761656744.136501 2236214 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/110

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Epoch 3/110

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Epoch 4/110

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Epoch 5/110

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Epoch 6/110

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[1m291/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1362 - loss: 2.7607
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1363 - loss: 2.7605 - val_accuracy: 0.2122 - val_loss: 2.3000
Epoch 7/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1346 - loss: 2.6978 
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[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1397 - loss: 2.6939
[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1403 - loss: 2.6933
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1409 - loss: 2.6920
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1414 - loss: 2.6903
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1420 - loss: 2.6884
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1424 - loss: 2.6869 - val_accuracy: 0.2220 - val_loss: 2.2725
Epoch 8/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1536 - loss: 2.6235 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1545 - loss: 2.6212
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1561 - loss: 2.6165
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1566 - loss: 2.6139
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1568 - loss: 2.6117
[1m275/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1569 - loss: 2.6099
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1570 - loss: 2.6092 - val_accuracy: 0.2244 - val_loss: 2.2364
Epoch 9/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1681 - loss: 2.5279 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1628 - loss: 2.5408
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1626 - loss: 2.5511 - val_accuracy: 0.2352 - val_loss: 2.2019
Epoch 10/110

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[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1639 - loss: 2.5396
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1658 - loss: 2.5229
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Epoch 11/110

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[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1770 - loss: 2.4553
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1768 - loss: 2.4539 - val_accuracy: 0.2496 - val_loss: 2.1377
Epoch 12/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1250 - loss: 2.5091
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1764 - loss: 2.4252 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1779 - loss: 2.4243
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1784 - loss: 2.4243
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1789 - loss: 2.4229
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1795 - loss: 2.4203
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1799 - loss: 2.4180
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1802 - loss: 2.4167 - val_accuracy: 0.2498 - val_loss: 2.1043
Epoch 13/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3125 - loss: 2.1685
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1804 - loss: 2.3910 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1774 - loss: 2.3999
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1772 - loss: 2.3993
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1784 - loss: 2.3964
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1793 - loss: 2.3939
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1801 - loss: 2.3916
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1811 - loss: 2.3889
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1816 - loss: 2.3874 - val_accuracy: 0.2638 - val_loss: 2.0741
Epoch 14/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1719 - loss: 2.3936
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1831 - loss: 2.3541 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1842 - loss: 2.3518
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[1m147/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1858 - loss: 2.3477
[1m181/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1873 - loss: 2.3451
[1m219/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1885 - loss: 2.3434
[1m250/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1894 - loss: 2.3423
[1m285/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1899 - loss: 2.3414
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Epoch 15/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2184 - loss: 2.2878 
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Epoch 16/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2123 - loss: 2.2526 
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Epoch 17/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1993 - loss: 2.2546 
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[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2066 - loss: 2.2495
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2069 - loss: 2.2489 - val_accuracy: 0.2809 - val_loss: 1.9769
Epoch 18/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2088 - loss: 2.2268 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2078 - loss: 2.2238
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[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2115 - loss: 2.2166
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2123 - loss: 2.2158
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2128 - loss: 2.2158
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2133 - loss: 2.2156
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2136 - loss: 2.2154 - val_accuracy: 0.2731 - val_loss: 1.9577
Epoch 19/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2381 - loss: 2.1989 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2360 - loss: 2.2005
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[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2307 - loss: 2.1989
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2294 - loss: 2.1988
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2285 - loss: 2.1983
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Epoch 20/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2222 - loss: 2.1852 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2237 - loss: 2.1821
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2228 - loss: 2.1790
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2226 - loss: 2.1781
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Epoch 21/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2409 - loss: 2.1267 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2395 - loss: 2.1305
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[1m140/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2343 - loss: 2.1412
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[1m213/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2321 - loss: 2.1449
[1m250/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2314 - loss: 2.1458
[1m286/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2308 - loss: 2.1463
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2307 - loss: 2.1464 - val_accuracy: 0.2969 - val_loss: 1.8974
Epoch 22/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2969 - loss: 1.9303
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2467 - loss: 2.0981 
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[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2372 - loss: 2.1219
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2356 - loss: 2.1246
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2349 - loss: 2.1258
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2346 - loss: 2.1263
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2345 - loss: 2.1266 - val_accuracy: 0.2969 - val_loss: 1.8794
Epoch 23/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2031 - loss: 2.2015
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2332 - loss: 2.1242 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2315 - loss: 2.1160
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2322 - loss: 2.1122
[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2330 - loss: 2.1102
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2342 - loss: 2.1097
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2350 - loss: 2.1099
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2358 - loss: 2.1098
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2360 - loss: 2.1099 - val_accuracy: 0.3182 - val_loss: 1.8773
Epoch 24/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1562 - loss: 2.1197
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2328 - loss: 2.1224 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2376 - loss: 2.1120
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2381 - loss: 2.1106
[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2386 - loss: 2.1081
[1m183/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2384 - loss: 2.1082
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2384 - loss: 2.1079
[1m263/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2387 - loss: 2.1072
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2391 - loss: 2.1067 - val_accuracy: 0.3080 - val_loss: 1.8673
Epoch 25/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2656 - loss: 1.9605
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2516 - loss: 2.0554 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2543 - loss: 2.0516
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2531 - loss: 2.0565
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2504 - loss: 2.0630
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2482 - loss: 2.0689
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2466 - loss: 2.0734
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2454 - loss: 2.0763
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Epoch 26/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2363 - loss: 2.0925 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2371 - loss: 2.0881
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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2409 - loss: 2.0786
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Epoch 27/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3594 - loss: 1.9799
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2509 - loss: 2.1050 
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[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2494 - loss: 2.0770
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2494 - loss: 2.0722 - val_accuracy: 0.3256 - val_loss: 1.8299
Epoch 28/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.2500 - loss: 2.1352
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2536 - loss: 2.0670 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2524 - loss: 2.0597
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[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2546 - loss: 2.0530
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2548 - loss: 2.0525
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2555 - loss: 2.0515
[1m262/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2559 - loss: 2.0510
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2560 - loss: 2.0507 - val_accuracy: 0.3175 - val_loss: 1.8230
Epoch 29/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1719 - loss: 2.2104
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2579 - loss: 2.0424 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2600 - loss: 2.0347
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2628 - loss: 2.0308
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2647 - loss: 2.0282
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2655 - loss: 2.0274
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2654 - loss: 2.0271
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2654 - loss: 2.0266
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2654 - loss: 2.0264 - val_accuracy: 0.3369 - val_loss: 1.8142
Epoch 30/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3281 - loss: 1.9252
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2732 - loss: 1.9853 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2708 - loss: 1.9994
[1m111/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2696 - loss: 2.0072
[1m146/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2689 - loss: 2.0109
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2683 - loss: 2.0129
[1m220/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2674 - loss: 2.0139
[1m259/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2666 - loss: 2.0153
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2661 - loss: 2.0168 - val_accuracy: 0.3317 - val_loss: 1.8103
Epoch 31/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1250 - loss: 2.0592
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2539 - loss: 2.0071 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2625 - loss: 2.0059
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2681 - loss: 2.0073
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Epoch 32/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2500 - loss: 2.0916
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2594 - loss: 2.0464 
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Epoch 33/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2969 - loss: 2.0352
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2904 - loss: 2.0031 
[1m 70/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2880 - loss: 2.0008
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[1m183/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2800 - loss: 2.0055
[1m222/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2786 - loss: 2.0057
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Epoch 34/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2656 - loss: 1.8943
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2686 - loss: 1.9448 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2686 - loss: 1.9578
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2688 - loss: 1.9646
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2692 - loss: 1.9702
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2693 - loss: 1.9743
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2696 - loss: 1.9765
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2697 - loss: 1.9779
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2697 - loss: 1.9787 - val_accuracy: 0.3484 - val_loss: 1.7681
Epoch 35/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2812 - loss: 2.0451
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2819 - loss: 1.9726 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2789 - loss: 1.9870
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2775 - loss: 1.9910
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2770 - loss: 1.9904
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2764 - loss: 1.9901
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2768 - loss: 1.9887
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2775 - loss: 1.9871
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2777 - loss: 1.9862 - val_accuracy: 0.3447 - val_loss: 1.7757
Epoch 36/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2344 - loss: 2.0492
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2844 - loss: 1.9891 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2844 - loss: 1.9911
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2836 - loss: 1.9907
[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2826 - loss: 1.9901
[1m197/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2823 - loss: 1.9894
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2825 - loss: 1.9882
[1m276/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2827 - loss: 1.9865
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Epoch 37/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2986 - loss: 1.9507 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2963 - loss: 1.9491
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Epoch 38/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2743 - loss: 1.9896 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2783 - loss: 1.9847
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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2866 - loss: 1.9689
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2874 - loss: 1.9666
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2877 - loss: 1.9655 - val_accuracy: 0.3443 - val_loss: 1.7603
Epoch 39/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2895 - loss: 1.9582 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2927 - loss: 1.9543
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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2913 - loss: 1.9531
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2912 - loss: 1.9525
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2912 - loss: 1.9520 - val_accuracy: 0.3460 - val_loss: 1.7430
Epoch 40/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.2188 - loss: 2.0117
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2976 - loss: 1.9326 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2966 - loss: 1.9302
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[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2964 - loss: 1.9301
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2959 - loss: 1.9301
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2957 - loss: 1.9299
[1m263/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2955 - loss: 1.9299
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2953 - loss: 1.9301 - val_accuracy: 0.3419 - val_loss: 1.7258
Epoch 41/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 25ms/step - accuracy: 0.3438 - loss: 1.8858
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2851 - loss: 1.9353 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2908 - loss: 1.9396
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[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2928 - loss: 1.9410
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2933 - loss: 1.9409
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2938 - loss: 1.9406
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2940 - loss: 1.9402
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Epoch 42/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3009 - loss: 1.9295 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3038 - loss: 1.9262
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Epoch 43/110

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Epoch 44/110

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[1m240/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3000 - loss: 1.9103
[1m275/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3006 - loss: 1.9102
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3009 - loss: 1.9103 - val_accuracy: 0.3525 - val_loss: 1.6996
Epoch 45/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2656 - loss: 1.8970
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2912 - loss: 1.9416 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2926 - loss: 1.9330
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[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2975 - loss: 1.9225
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2990 - loss: 1.9192
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2999 - loss: 1.9165
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3004 - loss: 1.9145
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3005 - loss: 1.9137 - val_accuracy: 0.3591 - val_loss: 1.6921
Epoch 46/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2656 - loss: 1.9020
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2756 - loss: 1.9101 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2871 - loss: 1.8906
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[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2951 - loss: 1.8875
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2964 - loss: 1.8880
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2975 - loss: 1.8895
[1m275/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2982 - loss: 1.8907
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2986 - loss: 1.8911 - val_accuracy: 0.3486 - val_loss: 1.6926
Epoch 47/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3125 - loss: 1.9217
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3088 - loss: 1.8706 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3122 - loss: 1.8733
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[1m146/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3128 - loss: 1.8798
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3123 - loss: 1.8822
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3119 - loss: 1.8830
[1m262/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3114 - loss: 1.8834
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Epoch 48/110

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Epoch 49/110

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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3061 - loss: 1.8915
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Epoch 50/110

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[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3132 - loss: 1.8593
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[1m241/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3085 - loss: 1.8712
[1m277/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3085 - loss: 1.8720
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3085 - loss: 1.8723 - val_accuracy: 0.3739 - val_loss: 1.6707
Epoch 51/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3412 - loss: 1.8218 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3284 - loss: 1.8383
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3218 - loss: 1.8537
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3209 - loss: 1.8553
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3202 - loss: 1.8569
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3198 - loss: 1.8579
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3197 - loss: 1.8583 - val_accuracy: 0.3591 - val_loss: 1.6723
Epoch 52/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3103 - loss: 1.8264 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3111 - loss: 1.8366
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3122 - loss: 1.8412
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3128 - loss: 1.8450
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3131 - loss: 1.8474
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3132 - loss: 1.8494
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Epoch 53/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2911 - loss: 1.9042 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3042 - loss: 1.8859
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Epoch 54/110

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Epoch 55/110

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Epoch 56/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2969 - loss: 1.8289
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3321 - loss: 1.8385 
[1m 65/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3314 - loss: 1.8411
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[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3278 - loss: 1.8509
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3276 - loss: 1.8504
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3277 - loss: 1.8495 - val_accuracy: 0.3567 - val_loss: 1.6511
Epoch 57/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3425 - loss: 1.7864 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3405 - loss: 1.7942
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[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3360 - loss: 1.8088
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3349 - loss: 1.8131
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3337 - loss: 1.8166
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3326 - loss: 1.8195
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3319 - loss: 1.8214 - val_accuracy: 0.3562 - val_loss: 1.6465
Epoch 58/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3246 - loss: 1.7908 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3266 - loss: 1.7981
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[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3264 - loss: 1.8137
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Epoch 59/110

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Epoch 60/110

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Epoch 61/110

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Epoch 62/110

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Epoch 63/110

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Epoch 64/110

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Epoch 65/110

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Epoch 66/110

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Epoch 67/110

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[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3217 - loss: 1.7912
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3273 - loss: 1.7962 - val_accuracy: 0.3748 - val_loss: 1.6259
Epoch 68/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3372 - loss: 1.7761 
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[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3381 - loss: 1.7853
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Epoch 69/110

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Epoch 70/110

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Epoch 71/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3247 - loss: 1.8035 
[1m 70/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3360 - loss: 1.7907
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[1m142/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3409 - loss: 1.7834
[1m181/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3414 - loss: 1.7827
[1m217/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3416 - loss: 1.7823
[1m257/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3419 - loss: 1.7823
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3420 - loss: 1.7825 - val_accuracy: 0.3730 - val_loss: 1.6156
Epoch 72/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3075 - loss: 1.7745 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3220 - loss: 1.7700
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[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3309 - loss: 1.7713
[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3330 - loss: 1.7715
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3346 - loss: 1.7713
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3353 - loss: 1.7720
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3357 - loss: 1.7732 - val_accuracy: 0.3724 - val_loss: 1.6119
Epoch 73/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.3281 - loss: 1.7415
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3352 - loss: 1.7874 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3348 - loss: 1.7849
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[1m146/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3365 - loss: 1.7782
[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3381 - loss: 1.7762
[1m222/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3391 - loss: 1.7755
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3398 - loss: 1.7757
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3401 - loss: 1.7759 - val_accuracy: 0.3869 - val_loss: 1.6145
Epoch 74/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3438 - loss: 1.7807
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3431 - loss: 1.8127 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3418 - loss: 1.8123
[1m106/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3415 - loss: 1.8090
[1m145/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3413 - loss: 1.8034
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3417 - loss: 1.7987
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3422 - loss: 1.7947
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3425 - loss: 1.7914
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3422 - loss: 1.7895 - val_accuracy: 0.3695 - val_loss: 1.6113
Epoch 75/110

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[1m 34/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3318 - loss: 1.7751 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3310 - loss: 1.7760
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[1m253/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3341 - loss: 1.7726
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Epoch 76/110

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[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3346 - loss: 1.7898
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[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3409 - loss: 1.7778
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Epoch 77/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3447 - loss: 1.7844 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3405 - loss: 1.7709
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3399 - loss: 1.7725
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3398 - loss: 1.7738
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3400 - loss: 1.7731
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3401 - loss: 1.7727 - val_accuracy: 0.3704 - val_loss: 1.6038
Epoch 78/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3281 - loss: 1.7462
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3511 - loss: 1.7660 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3471 - loss: 1.7616
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3464 - loss: 1.7636
[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3462 - loss: 1.7640
[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3458 - loss: 1.7641
[1m218/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3455 - loss: 1.7639
[1m256/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3450 - loss: 1.7641
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3446 - loss: 1.7640 - val_accuracy: 0.3739 - val_loss: 1.6089
Epoch 79/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.3750 - loss: 1.7585
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3439 - loss: 1.7361 
[1m 70/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3471 - loss: 1.7395
[1m109/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3478 - loss: 1.7432
[1m146/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3478 - loss: 1.7446
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3474 - loss: 1.7463
[1m220/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3469 - loss: 1.7475
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3465 - loss: 1.7489
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3463 - loss: 1.7498 - val_accuracy: 0.3645 - val_loss: 1.6088
Epoch 80/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3448 - loss: 1.8044 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3515 - loss: 1.7884
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[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3519 - loss: 1.7767
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[1m237/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3521 - loss: 1.7694
[1m277/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3520 - loss: 1.7667
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3520 - loss: 1.7659 - val_accuracy: 0.3774 - val_loss: 1.6041

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:12[0m 849ms/step
[1m68/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 750us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
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Saved model to disk.

Accuracy capturado en la ejecución 4: 36.32 [%]
F1-score capturado en la ejecución 4: 36.2 [%]

=== EJECUCIÓN 5 ===

--- TRAIN (ejecución 5) ---

--- TEST (ejecución 5) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18676, 6, 250)

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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m69/86[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 742us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 8ms/step

[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 62/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 826us/step
[1m134/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 756us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 37.74 [%]
Global F1 score (validation) = 36.6 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[2.0831169e-01 1.9889925e-01 2.0112573e-01 ... 4.1488038e-06
  1.6192695e-01 2.1685548e-02]
 [2.1643043e-01 2.0392267e-01 2.1002308e-01 ... 7.5958278e-06
  1.3729194e-01 1.4800007e-02]
 [1.8267089e-01 1.5277870e-01 1.6744561e-01 ... 4.7743588e-06
  1.5737088e-01 1.5400007e-01]
 ...
 [1.9487788e-01 1.9831531e-01 1.9701239e-01 ... 8.9288871e-05
  1.7459805e-01 3.8838767e-02]
 [1.8830830e-01 2.0437461e-01 2.0246805e-01 ... 2.4557047e-04
  1.7366996e-01 1.6281199e-02]
 [1.5564220e-01 1.8877269e-01 1.7313875e-01 ... 5.9669220e-04
  2.0255473e-01 1.7021300e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 43.55 [%]
Global accuracy score (test) = 35.99 [%]
Global F1 score (train) = 41.49 [%]
Global F1 score (test) = 34.67 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.27      0.54      0.36       184
 CAMINAR CON MÓVIL O LIBRO       0.13      0.16      0.14       184
       CAMINAR USUAL SPEED       0.16      0.04      0.06       184
            CAMINAR ZIGZAG       0.20      0.18      0.19       184
          DE PIE BARRIENDO       0.27      0.43      0.33       184
   DE PIE DOBLANDO TOALLAS       0.19      0.08      0.11       184
    DE PIE MOVIENDO LIBROS       0.30      0.40      0.34       184
          DE PIE USANDO PC       0.42      0.62      0.51       184
        FASE REPOSO CON K5       0.58      0.75      0.66       184
INCREMENTAL CICLOERGOMETRO       0.92      0.60      0.73       184
           SENTADO LEYENDO       0.33      0.40      0.36       184
         SENTADO USANDO PC       0.18      0.12      0.14       184
      SENTADO VIENDO LA TV       0.37      0.21      0.26       184
   SUBIR Y BAJAR ESCALERAS       0.37      0.18      0.25       184
                    TROTAR       0.80      0.73      0.76       161

                  accuracy                           0.36      2737
                 macro avg       0.37      0.36      0.35      2737
              weighted avg       0.36      0.36      0.34      2737

2025-10-28 14:06:45.821692: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 14:06:45.833256: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1761656805.846681 2244567 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761656805.850994 2244567 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761656805.861096 2244567 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761656805.861116 2244567 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761656805.861119 2244567 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761656805.861129 2244567 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 14:06:45.864373: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1761656808.317399 2244567 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/110
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761656810.067667 2244686 service.cc:152] XLA service 0x70bcdc01d2d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761656810.067729 2244686 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 14:06:50.111316: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
I0000 00:00:1761656810.281566 2244686 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761656812.545327 2244686 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16:50[0m 3s/step - accuracy: 0.1094 - loss: 3.4222
[1m 33/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0666 - loss: 3.5541  
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Epoch 2/110

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Epoch 3/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1070 - loss: 3.0473 
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1144 - loss: 3.0243 - val_accuracy: 0.1832 - val_loss: 2.3915
Epoch 4/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1329 - loss: 2.9207 
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[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1316 - loss: 2.9047
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[1m219/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1311 - loss: 2.8969
[1m257/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1308 - loss: 2.8937
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1308 - loss: 2.8909 - val_accuracy: 0.1826 - val_loss: 2.3547
Epoch 5/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1453 - loss: 2.8301 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1417 - loss: 2.8268
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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1385 - loss: 2.8157
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Epoch 6/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1618 - loss: 2.6808 
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Epoch 7/110

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Epoch 8/110

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[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1622 - loss: 2.6074
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1626 - loss: 2.6011 - val_accuracy: 0.2196 - val_loss: 2.2065
Epoch 9/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1658 - loss: 2.5611 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1674 - loss: 2.5547
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[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1661 - loss: 2.5449
[1m263/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1660 - loss: 2.5431
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1661 - loss: 2.5413 - val_accuracy: 0.2307 - val_loss: 2.1651
Epoch 10/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1693 - loss: 2.4874 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1721 - loss: 2.4877
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1747 - loss: 2.4850
[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1760 - loss: 2.4836
[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1764 - loss: 2.4826
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1766 - loss: 2.4818
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1768 - loss: 2.4806
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1769 - loss: 2.4797 - val_accuracy: 0.2453 - val_loss: 2.1265
Epoch 11/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1821 - loss: 2.4791 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1837 - loss: 2.4617
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[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1835 - loss: 2.4484
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1835 - loss: 2.4460
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Epoch 12/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1975 - loss: 2.4102 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1945 - loss: 2.4075
[1m105/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1940 - loss: 2.4032
[1m141/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1940 - loss: 2.4003
[1m180/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1940 - loss: 2.3983
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[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1934 - loss: 2.3947
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Epoch 13/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1877 - loss: 2.3557 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1905 - loss: 2.3481
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[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1939 - loss: 2.3396
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1943 - loss: 2.3379
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1946 - loss: 2.3366
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1948 - loss: 2.3359 - val_accuracy: 0.2525 - val_loss: 2.0274
Epoch 14/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1857 - loss: 2.3440 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1918 - loss: 2.3307
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1954 - loss: 2.3244
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1976 - loss: 2.3200
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1987 - loss: 2.3171
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1998 - loss: 2.3147
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2005 - loss: 2.3132
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2008 - loss: 2.3125 - val_accuracy: 0.2623 - val_loss: 2.0061
Epoch 15/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1875 - loss: 2.3506
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2121 - loss: 2.2900 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2086 - loss: 2.2930
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2081 - loss: 2.2919
[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2083 - loss: 2.2892
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2085 - loss: 2.2866
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2086 - loss: 2.2842
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2085 - loss: 2.2824
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2085 - loss: 2.2816 - val_accuracy: 0.2664 - val_loss: 1.9731
Epoch 16/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2031 - loss: 2.3780
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2010 - loss: 2.3116 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2046 - loss: 2.2870
[1m109/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2078 - loss: 2.2739
[1m146/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2088 - loss: 2.2665
[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2094 - loss: 2.2612
[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2100 - loss: 2.2579
[1m258/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2107 - loss: 2.2548
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2114 - loss: 2.2523 - val_accuracy: 0.2753 - val_loss: 1.9511
Epoch 17/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2167 - loss: 2.2449 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2149 - loss: 2.2344
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[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2116 - loss: 2.2298
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2121 - loss: 2.2279
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2124 - loss: 2.2265 - val_accuracy: 0.2821 - val_loss: 1.9405
Epoch 18/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2341 - loss: 2.1804 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2251 - loss: 2.1888
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[1m217/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2244 - loss: 2.1888
[1m255/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2247 - loss: 2.1886
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2249 - loss: 2.1885 - val_accuracy: 0.2727 - val_loss: 1.9258
Epoch 19/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1875 - loss: 2.2116
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2313 - loss: 2.1618 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2318 - loss: 2.1671
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[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2301 - loss: 2.1736
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2295 - loss: 2.1757
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2290 - loss: 2.1759
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2287 - loss: 2.1754
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2285 - loss: 2.1749 - val_accuracy: 0.2862 - val_loss: 1.9201
Epoch 20/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2188 - loss: 2.2092
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2337 - loss: 2.1539 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2320 - loss: 2.1484
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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2301 - loss: 2.1467
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2295 - loss: 2.1467
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2295 - loss: 2.1465
[1m259/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2297 - loss: 2.1461
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2299 - loss: 2.1462 - val_accuracy: 0.2907 - val_loss: 1.8910
Epoch 21/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2500 - loss: 2.0842
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2247 - loss: 2.1371 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2307 - loss: 2.1293
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2326 - loss: 2.1291
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2338 - loss: 2.1287
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2339 - loss: 2.1285
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2338 - loss: 2.1284
[1m262/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2337 - loss: 2.1283
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2338 - loss: 2.1284 - val_accuracy: 0.2969 - val_loss: 1.8827
Epoch 22/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2344 - loss: 2.0895
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2433 - loss: 2.1050 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2403 - loss: 2.1029
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2387 - loss: 2.1054
[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2372 - loss: 2.1078
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2364 - loss: 2.1099
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2361 - loss: 2.1123
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2361 - loss: 2.1136
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Epoch 23/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2656 - loss: 2.1330
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2429 - loss: 2.1091 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2463 - loss: 2.1030
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[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2477 - loss: 2.0975
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Epoch 24/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2031 - loss: 2.0822
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2443 - loss: 2.0616 
[1m 68/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2431 - loss: 2.0694
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[1m143/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2423 - loss: 2.0765
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[1m220/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2431 - loss: 2.0775
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2434 - loss: 2.0782
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.2439 - loss: 2.0786
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2439 - loss: 2.0786 - val_accuracy: 0.3166 - val_loss: 1.8338
Epoch 25/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3125 - loss: 1.8988
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2594 - loss: 2.0907 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2520 - loss: 2.0951
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[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2473 - loss: 2.0949
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2472 - loss: 2.0934
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2472 - loss: 2.0923
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2473 - loss: 2.0912
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2475 - loss: 2.0904 - val_accuracy: 0.3117 - val_loss: 1.8241
Epoch 26/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3281 - loss: 2.2118
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2773 - loss: 2.0677 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2681 - loss: 2.0712
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2630 - loss: 2.0710
[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2603 - loss: 2.0692
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2591 - loss: 2.0677
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2580 - loss: 2.0666
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2572 - loss: 2.0660
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2569 - loss: 2.0656 - val_accuracy: 0.3018 - val_loss: 1.8262
Epoch 27/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2031 - loss: 2.1039
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2528 - loss: 2.0406 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2556 - loss: 2.0366
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2566 - loss: 2.0343
[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2564 - loss: 2.0362
[1m199/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2565 - loss: 2.0382
[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2568 - loss: 2.0396
[1m275/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2568 - loss: 2.0407
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2568 - loss: 2.0411 - val_accuracy: 0.3179 - val_loss: 1.8089
Epoch 28/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.2812 - loss: 2.2176
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2703 - loss: 2.0515 
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2681 - loss: 2.0381
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2675 - loss: 2.0386
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2671 - loss: 2.0384
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2670 - loss: 2.0377
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Epoch 29/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2614 - loss: 2.0377 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2680 - loss: 2.0308
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2669 - loss: 2.0253
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Epoch 30/110

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[1m 34/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2739 - loss: 1.9960 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2732 - loss: 1.9996
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[1m146/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2704 - loss: 2.0098
[1m181/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2702 - loss: 2.0114
[1m217/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2702 - loss: 2.0118
[1m253/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2701 - loss: 2.0120
[1m290/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2701 - loss: 2.0119
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2701 - loss: 2.0119 - val_accuracy: 0.3273 - val_loss: 1.7865
Epoch 31/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2656 - loss: 2.0560
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2634 - loss: 2.0563 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2681 - loss: 2.0328
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2679 - loss: 2.0241
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2684 - loss: 2.0177
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2693 - loss: 2.0144
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2698 - loss: 2.0127
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2702 - loss: 2.0111
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2704 - loss: 2.0102 - val_accuracy: 0.3273 - val_loss: 1.7742
Epoch 32/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2031 - loss: 2.0794
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2576 - loss: 2.0067 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2624 - loss: 2.0043
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2652 - loss: 2.0037
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2674 - loss: 2.0027
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2694 - loss: 2.0019
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2708 - loss: 2.0003
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2718 - loss: 1.9989
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2722 - loss: 1.9981 - val_accuracy: 0.3301 - val_loss: 1.7652
Epoch 33/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2812 - loss: 1.9081
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2953 - loss: 1.9761 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2922 - loss: 1.9758
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[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2909 - loss: 1.9793
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Epoch 34/110

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Epoch 35/110

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Epoch 36/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2812 - loss: 1.8741
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3004 - loss: 1.9281 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2967 - loss: 1.9404
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2905 - loss: 1.9501 - val_accuracy: 0.3417 - val_loss: 1.7308
Epoch 37/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2500 - loss: 2.0679
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2773 - loss: 1.9905 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2789 - loss: 1.9785
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2859 - loss: 1.9625
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[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2888 - loss: 1.9570
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2896 - loss: 1.9546
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Epoch 38/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2948 - loss: 1.9323 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2985 - loss: 1.9297
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[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2960 - loss: 1.9343
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Epoch 39/110

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Epoch 40/110

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Epoch 41/110

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Epoch 42/110

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Epoch 43/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3071 - loss: 1.9301 
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Epoch 44/110

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Epoch 45/110

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Epoch 46/110

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Epoch 47/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3311 - loss: 1.8231 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3210 - loss: 1.8402
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3157 - loss: 1.8624
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3158 - loss: 1.8648 - val_accuracy: 0.3602 - val_loss: 1.6833
Epoch 48/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3091 - loss: 1.8813 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3112 - loss: 1.8688
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[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3148 - loss: 1.8639
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3155 - loss: 1.8641
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3158 - loss: 1.8644
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Epoch 49/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3309 - loss: 1.8747 
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[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3252 - loss: 1.8600
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3250 - loss: 1.8599
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Epoch 50/110

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Epoch 51/110

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Epoch 52/110

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Epoch 53/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3117 - loss: 1.8646 
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3199 - loss: 1.8441
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3202 - loss: 1.8446 - val_accuracy: 0.3680 - val_loss: 1.6450
Epoch 54/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3059 - loss: 1.8855 
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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3167 - loss: 1.8554
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3178 - loss: 1.8534
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Epoch 55/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3247 - loss: 1.8497 
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Epoch 56/110

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Epoch 57/110

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Epoch 58/110

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[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3343 - loss: 1.8256
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3343 - loss: 1.8249 - val_accuracy: 0.3647 - val_loss: 1.6365
Epoch 59/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3354 - loss: 1.8184 
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3351 - loss: 1.8103
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3348 - loss: 1.8109
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Epoch 60/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3353 - loss: 1.7850 
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Epoch 61/110

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Epoch 62/110

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Epoch 63/110

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Epoch 64/110

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Epoch 65/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3427 - loss: 1.8353 
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Epoch 66/110

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Epoch 67/110

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Epoch 68/110

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Epoch 69/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3260 - loss: 1.7943 
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3352 - loss: 1.7937 - val_accuracy: 0.3728 - val_loss: 1.6166
Epoch 70/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3374 - loss: 1.7519 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3407 - loss: 1.7645
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[1m218/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3399 - loss: 1.7798
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Epoch 71/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3478 - loss: 1.7428 
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Epoch 72/110

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Epoch 73/110

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[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3404 - loss: 1.8019
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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3415 - loss: 1.7849
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Epoch 74/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3495 - loss: 1.7374 
[1m 70/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3465 - loss: 1.7383
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[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3430 - loss: 1.7531
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3427 - loss: 1.7567
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3423 - loss: 1.7597
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3421 - loss: 1.7614 - val_accuracy: 0.3743 - val_loss: 1.6026
Epoch 75/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3464 - loss: 1.7441 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3467 - loss: 1.7584
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3478 - loss: 1.7623
[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3477 - loss: 1.7643
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3466 - loss: 1.7663
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3461 - loss: 1.7672
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3458 - loss: 1.7678
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3458 - loss: 1.7681 - val_accuracy: 0.3693 - val_loss: 1.6146
Epoch 76/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3394 - loss: 1.8093 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3422 - loss: 1.8003
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[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3460 - loss: 1.7825
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3475 - loss: 1.7778
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3480 - loss: 1.7757
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Epoch 77/110

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Epoch 78/110

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Epoch 79/110

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Saved model to disk.

Accuracy capturado en la ejecución 5: 35.99 [%]
F1-score capturado en la ejecución 5: 34.67 [%]

=== EJECUCIÓN 6 ===

--- TRAIN (ejecución 6) ---

--- TEST (ejecución 6) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18676, 6, 250)

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[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 37.8 [%]
Global F1 score (validation) = 37.32 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[2.03985780e-01 1.87705413e-01 1.90431371e-01 ... 2.92188588e-06
  1.84831783e-01 1.93448160e-02]
 [2.12128401e-01 1.96380258e-01 2.15721428e-01 ... 3.06089350e-06
  1.49018288e-01 1.53516969e-02]
 [2.03606084e-01 1.83230445e-01 2.11692289e-01 ... 4.30223736e-06
  1.57206655e-01 4.60309088e-02]
 ...
 [1.84661344e-01 2.08418727e-01 1.99652299e-01 ... 8.23433860e-04
  1.51948452e-01 1.76258013e-02]
 [1.85061365e-01 2.10423946e-01 1.94414914e-01 ... 8.55227700e-05
  1.69730589e-01 1.39128221e-02]
 [7.50645474e-02 1.11214392e-01 8.85970369e-02 ... 7.00625870e-03
  1.00228384e-01 7.16777286e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 44.16 [%]
Global accuracy score (test) = 36.32 [%]
Global F1 score (train) = 42.61 [%]
Global F1 score (test) = 34.9 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.23      0.36      0.28       184
 CAMINAR CON MÓVIL O LIBRO       0.22      0.33      0.26       184
       CAMINAR USUAL SPEED       0.22      0.24      0.23       184
            CAMINAR ZIGZAG       0.11      0.01      0.01       184
          DE PIE BARRIENDO       0.29      0.51      0.37       184
   DE PIE DOBLANDO TOALLAS       0.32      0.12      0.18       184
    DE PIE MOVIENDO LIBROS       0.33      0.41      0.37       184
          DE PIE USANDO PC       0.42      0.62      0.50       184
        FASE REPOSO CON K5       0.52      0.76      0.62       184
INCREMENTAL CICLOERGOMETRO       0.91      0.60      0.72       184
           SENTADO LEYENDO       0.23      0.21      0.22       184
         SENTADO USANDO PC       0.22      0.11      0.15       184
      SENTADO VIENDO LA TV       0.39      0.37      0.38       184
   SUBIR Y BAJAR ESCALERAS       0.23      0.14      0.18       184
                    TROTAR       0.85      0.70      0.76       161

                  accuracy                           0.36      2737
                 macro avg       0.37      0.37      0.35      2737
              weighted avg       0.36      0.36      0.35      2737

2025-10-28 14:07:53.265410: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 14:07:53.277142: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1761656873.290721 2252967 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761656873.294808 2252967 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761656873.305024 2252967 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761656873.305046 2252967 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761656873.305049 2252967 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761656873.305050 2252967 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 14:07:53.308419: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1761656875.709719 2252967 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13748 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/110
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761656877.513262 2253082 service.cc:152] XLA service 0x73469800c930 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761656877.513305 2253082 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 14:07:57.547307: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
I0000 00:00:1761656877.732658 2253082 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761656880.198140 2253082 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18:05[0m 4s/step - accuracy: 0.0469 - loss: 3.5577
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[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0701 - loss: 3.5664
[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0724 - loss: 3.5463
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[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0772 - loss: 3.5100
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.0790 - loss: 3.4944
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step - accuracy: 0.0800 - loss: 3.4849
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 16ms/step - accuracy: 0.0800 - loss: 3.4846 - val_accuracy: 0.1654 - val_loss: 2.4793
Epoch 2/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1098 - loss: 3.1998 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1085 - loss: 3.2002
[1m111/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1073 - loss: 3.2024
[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1068 - loss: 3.2011
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1065 - loss: 3.1979
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1064 - loss: 3.1941
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1063 - loss: 3.1907
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Epoch 3/110

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[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1113 - loss: 3.0783
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1120 - loss: 3.0744
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Epoch 4/110

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Epoch 5/110

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Epoch 6/110

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Epoch 7/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1536 - loss: 2.6964 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1539 - loss: 2.6864
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1550 - loss: 2.6686
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1548 - loss: 2.6663
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1548 - loss: 2.6650 - val_accuracy: 0.1989 - val_loss: 2.2566
Epoch 8/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1599 - loss: 2.5768 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1599 - loss: 2.5815
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[1m142/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1579 - loss: 2.5880
[1m179/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1574 - loss: 2.5889
[1m217/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1571 - loss: 2.5894
[1m253/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1569 - loss: 2.5894
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Epoch 9/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1433 - loss: 2.5532 
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Epoch 10/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1570 - loss: 2.5320 
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Epoch 11/110

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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1847 - loss: 2.4287
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1845 - loss: 2.4276
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1844 - loss: 2.4270 - val_accuracy: 0.2511 - val_loss: 2.0850
Epoch 12/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 25ms/step - accuracy: 0.2344 - loss: 2.3278
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1967 - loss: 2.3962 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1928 - loss: 2.3896
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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1901 - loss: 2.3895
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1895 - loss: 2.3880
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1892 - loss: 2.3863
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1889 - loss: 2.3849
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1887 - loss: 2.3839 - val_accuracy: 0.2712 - val_loss: 2.0398
Epoch 13/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1562 - loss: 2.4911
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1802 - loss: 2.3870 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1818 - loss: 2.3717
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1840 - loss: 2.3625
[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1861 - loss: 2.3555
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1881 - loss: 2.3498
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1894 - loss: 2.3456
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1905 - loss: 2.3421
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1911 - loss: 2.3402 - val_accuracy: 0.2631 - val_loss: 2.0230
Epoch 14/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2500 - loss: 2.1775
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2001 - loss: 2.3328 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2061 - loss: 2.3248
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[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2070 - loss: 2.3147
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Epoch 15/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1823 - loss: 2.2886 
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[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1940 - loss: 2.2803
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1961 - loss: 2.2769 - val_accuracy: 0.2923 - val_loss: 1.9595
Epoch 16/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2031 - loss: 2.1613
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2136 - loss: 2.2206 
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[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2137 - loss: 2.2228
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2137 - loss: 2.2231
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2138 - loss: 2.2227 - val_accuracy: 0.2997 - val_loss: 1.9353
Epoch 17/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2812 - loss: 2.1509
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2256 - loss: 2.2035 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2207 - loss: 2.2063
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[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2189 - loss: 2.2073
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2187 - loss: 2.2071
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2184 - loss: 2.2077
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2184 - loss: 2.2077
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2184 - loss: 2.2075 - val_accuracy: 0.2997 - val_loss: 1.9141
Epoch 18/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.2344 - loss: 2.2988
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2017 - loss: 2.2214 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2066 - loss: 2.2040
[1m120/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2084 - loss: 2.1989
[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2105 - loss: 2.1958
[1m199/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2127 - loss: 2.1933
[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2142 - loss: 2.1913
[1m279/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2152 - loss: 2.1894
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2155 - loss: 2.1889 - val_accuracy: 0.3073 - val_loss: 1.8966
Epoch 19/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2188 - loss: 2.3272
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2234 - loss: 2.1764 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2242 - loss: 2.1717
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2252 - loss: 2.1693
[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2267 - loss: 2.1652
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2275 - loss: 2.1633
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2279 - loss: 2.1623
[1m275/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2282 - loss: 2.1621
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2283 - loss: 2.1620 - val_accuracy: 0.3184 - val_loss: 1.8796
Epoch 20/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2481 - loss: 2.1135 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2430 - loss: 2.1177
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2397 - loss: 2.1245
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2393 - loss: 2.1252
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2392 - loss: 2.1253 - val_accuracy: 0.3038 - val_loss: 1.8759
Epoch 21/110

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[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2284 - loss: 2.1168
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2314 - loss: 2.1139
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2323 - loss: 2.1134 - val_accuracy: 0.3210 - val_loss: 1.8542
Epoch 22/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2265 - loss: 2.1322 
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2351 - loss: 2.1106
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2359 - loss: 2.1090
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2361 - loss: 2.1084 - val_accuracy: 0.3278 - val_loss: 1.8472
Epoch 23/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1094 - loss: 2.4191
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2397 - loss: 2.1243 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2409 - loss: 2.1083
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[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2443 - loss: 2.0980
[1m198/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2449 - loss: 2.0956
[1m240/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2454 - loss: 2.0932
[1m277/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2457 - loss: 2.0914
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2458 - loss: 2.0909 - val_accuracy: 0.3103 - val_loss: 1.8318
Epoch 24/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2031 - loss: 2.0231
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2492 - loss: 2.0570 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2479 - loss: 2.0683
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2474 - loss: 2.0708
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2470 - loss: 2.0716
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2470 - loss: 2.0709
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2469 - loss: 2.0710
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2469 - loss: 2.0711
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2469 - loss: 2.0712 - val_accuracy: 0.3105 - val_loss: 1.8278
Epoch 25/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.2969 - loss: 2.0298
[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2621 - loss: 2.0330 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2571 - loss: 2.0486
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[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2532 - loss: 2.0555
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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2517 - loss: 2.0564
[1m275/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2513 - loss: 2.0564
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2511 - loss: 2.0564 - val_accuracy: 0.3166 - val_loss: 1.8228
Epoch 26/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1094 - loss: 2.1641
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2465 - loss: 2.0167 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2506 - loss: 2.0226
[1m111/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2524 - loss: 2.0247
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2530 - loss: 2.0282
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2531 - loss: 2.0308
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2530 - loss: 2.0326
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2527 - loss: 2.0340
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2525 - loss: 2.0346 - val_accuracy: 0.3105 - val_loss: 1.8116
Epoch 27/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2500 - loss: 1.9375
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2497 - loss: 2.0489 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2525 - loss: 2.0441
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2550 - loss: 2.0388
[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2569 - loss: 2.0366
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2581 - loss: 2.0362
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2586 - loss: 2.0367
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2589 - loss: 2.0365
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2591 - loss: 2.0363 - val_accuracy: 0.3258 - val_loss: 1.8029
Epoch 28/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3125 - loss: 1.9144
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2617 - loss: 2.0407 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2597 - loss: 2.0366
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2585 - loss: 2.0341
[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2578 - loss: 2.0321
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2575 - loss: 2.0315
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2572 - loss: 2.0312
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2573 - loss: 2.0306
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2573 - loss: 2.0302 - val_accuracy: 0.3210 - val_loss: 1.7991
Epoch 29/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3281 - loss: 1.9173
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2602 - loss: 2.0140 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2609 - loss: 2.0181
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2609 - loss: 2.0195
[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2614 - loss: 2.0175
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2620 - loss: 2.0158
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2625 - loss: 2.0154
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2626 - loss: 2.0154
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2628 - loss: 2.0152 - val_accuracy: 0.3332 - val_loss: 1.7893
Epoch 30/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2031 - loss: 2.1002
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2570 - loss: 2.0188 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2606 - loss: 2.0139
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2622 - loss: 2.0112
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2628 - loss: 2.0097
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2640 - loss: 2.0075
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2649 - loss: 2.0058
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2660 - loss: 2.0043
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2666 - loss: 2.0040 - val_accuracy: 0.3389 - val_loss: 1.7865
Epoch 31/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2757 - loss: 1.9978 
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Epoch 32/110

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Epoch 33/110

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[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2823 - loss: 1.9520
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2824 - loss: 1.9524 - val_accuracy: 0.3340 - val_loss: 1.7622
Epoch 34/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2897 - loss: 2.0084 
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[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2812 - loss: 1.9845
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2812 - loss: 1.9821
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2812 - loss: 1.9810 - val_accuracy: 0.3362 - val_loss: 1.7617
Epoch 35/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2843 - loss: 1.9806 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2797 - loss: 1.9744
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2800 - loss: 1.9700
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2801 - loss: 1.9688
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Epoch 36/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2802 - loss: 1.9716 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2853 - loss: 1.9522
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Epoch 37/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2943 - loss: 1.9030 
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Epoch 38/110

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[1m 42/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2923 - loss: 1.9314 
[1m 83/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2903 - loss: 1.9378
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[1m241/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2908 - loss: 1.9379
[1m280/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2907 - loss: 1.9379
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2908 - loss: 1.9378 - val_accuracy: 0.3482 - val_loss: 1.7374
Epoch 39/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3004 - loss: 1.9060 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2985 - loss: 1.9175
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[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2956 - loss: 1.9292
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2947 - loss: 1.9307
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2944 - loss: 1.9314 - val_accuracy: 0.3447 - val_loss: 1.7328
Epoch 40/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3438 - loss: 1.8577
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3076 - loss: 1.9387 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3036 - loss: 1.9354
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[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2999 - loss: 1.9332
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2991 - loss: 1.9325
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2985 - loss: 1.9316
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2980 - loss: 1.9307
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2978 - loss: 1.9302 - val_accuracy: 0.3458 - val_loss: 1.7252
Epoch 41/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3158 - loss: 1.8839 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3125 - loss: 1.8858
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3096 - loss: 1.8925
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[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3054 - loss: 1.9030
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3045 - loss: 1.9055
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Epoch 42/110

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Epoch 43/110

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Epoch 44/110

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Epoch 45/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3009 - loss: 1.9033 
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[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3072 - loss: 1.8876
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Epoch 46/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2863 - loss: 1.9298 
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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3056 - loss: 1.8870
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3061 - loss: 1.8849
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Epoch 47/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3040 - loss: 1.8826 
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Epoch 48/110

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Epoch 49/110

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Epoch 50/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3217 - loss: 1.8792 
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3143 - loss: 1.8644
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3139 - loss: 1.8642
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3138 - loss: 1.8640 - val_accuracy: 0.3495 - val_loss: 1.6803
Epoch 51/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3230 - loss: 1.8370 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3203 - loss: 1.8380
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[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3213 - loss: 1.8388
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3220 - loss: 1.8404
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3222 - loss: 1.8426
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3223 - loss: 1.8439 - val_accuracy: 0.3510 - val_loss: 1.6756
Epoch 52/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3194 - loss: 1.8299 
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Epoch 53/110

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Epoch 54/110

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Epoch 55/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3264 - loss: 1.8576 
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3226 - loss: 1.8460
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3222 - loss: 1.8442 - val_accuracy: 0.3591 - val_loss: 1.6571
Epoch 56/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3401 - loss: 1.7989 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3364 - loss: 1.8050
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[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3310 - loss: 1.8203
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3305 - loss: 1.8233
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3301 - loss: 1.8256
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Epoch 57/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3305 - loss: 1.8359 
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3304 - loss: 1.8277
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Epoch 58/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3188 - loss: 1.8445 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3238 - loss: 1.8342
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[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3262 - loss: 1.8273
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3264 - loss: 1.8257 - val_accuracy: 0.3491 - val_loss: 1.6549
Epoch 59/110

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[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3353 - loss: 1.8131
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Epoch 60/110

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Epoch 61/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2656 - loss: 1.9142
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3302 - loss: 1.8209 - val_accuracy: 0.3526 - val_loss: 1.6548

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:13[0m 864ms/step
[1m67/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 759us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 9ms/step
Saved model to disk.

Accuracy capturado en la ejecución 6: 36.32 [%]
F1-score capturado en la ejecución 6: 34.9 [%]

=== EJECUCIÓN 7 ===

--- TRAIN (ejecución 7) ---

--- TEST (ejecución 7) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18676, 6, 250)

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[1m 64/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 803us/step
[1m135/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 755us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 35.26 [%]
Global F1 score (validation) = 34.48 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[2.1615365e-01 1.8389879e-01 2.1364914e-01 ... 4.9631058e-06
  1.3706020e-01 4.3518838e-02]
 [2.0050724e-01 1.9982661e-01 1.9934562e-01 ... 1.8979395e-05
  1.5942647e-01 2.1841194e-02]
 [1.8023168e-01 1.5710351e-01 1.9151349e-01 ... 2.3537787e-05
  1.6430144e-01 1.3149215e-01]
 ...
 [2.0580624e-01 1.7888354e-01 1.9630037e-01 ... 7.7878431e-05
  1.5040863e-01 5.3807199e-02]
 [1.8404496e-01 2.1212125e-01 2.0106079e-01 ... 4.2657259e-05
  1.7936148e-01 1.8280750e-02]
 [1.2952705e-01 1.7715606e-01 1.3333797e-01 ... 5.4664188e-04
  1.3777685e-01 5.2549057e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 42.61 [%]
Global accuracy score (test) = 36.32 [%]
Global F1 score (train) = 40.55 [%]
Global F1 score (test) = 35.01 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.37      0.38      0.38       184
 CAMINAR CON MÓVIL O LIBRO       0.20      0.36      0.26       184
       CAMINAR USUAL SPEED       0.24      0.24      0.24       184
            CAMINAR ZIGZAG       0.17      0.14      0.15       184
          DE PIE BARRIENDO       0.25      0.60      0.36       184
   DE PIE DOBLANDO TOALLAS       0.36      0.02      0.04       184
    DE PIE MOVIENDO LIBROS       0.24      0.31      0.27       184
          DE PIE USANDO PC       0.41      0.56      0.47       184
        FASE REPOSO CON K5       0.62      0.84      0.72       184
INCREMENTAL CICLOERGOMETRO       0.89      0.59      0.71       184
           SENTADO LEYENDO       0.25      0.22      0.23       184
         SENTADO USANDO PC       0.22      0.09      0.13       184
      SENTADO VIENDO LA TV       0.40      0.36      0.38       184
   SUBIR Y BAJAR ESCALERAS       0.34      0.10      0.15       184
                    TROTAR       0.88      0.66      0.76       161

                  accuracy                           0.36      2737
                 macro avg       0.39      0.37      0.35      2737
              weighted avg       0.39      0.36      0.35      2737

2025-10-28 14:08:50.941662: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 14:08:50.952961: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1761656930.966364 2259688 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761656930.970623 2259688 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761656930.980476 2259688 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761656930.980500 2259688 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761656930.980503 2259688 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761656930.980514 2259688 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 14:08:50.983732: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1761656933.370251 2259688 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13697 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/110
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761656935.080899 2259813 service.cc:152] XLA service 0x71b9fc01e090 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761656935.080962 2259813 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 14:08:55.120369: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
I0000 00:00:1761656935.290773 2259813 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761656937.577707 2259813 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16:49[0m 3s/step - accuracy: 0.0625 - loss: 3.4376
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[1m222/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0815 - loss: 3.4829
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0826 - loss: 3.4714
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step - accuracy: 0.0835 - loss: 3.4628
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 16ms/step - accuracy: 0.0835 - loss: 3.4626 - val_accuracy: 0.1615 - val_loss: 2.4875
Epoch 2/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0979 - loss: 3.2638 
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[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0991 - loss: 3.2420
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0995 - loss: 3.2375
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Epoch 3/110

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[1m218/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1195 - loss: 3.0651
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1197 - loss: 3.0561 - val_accuracy: 0.1700 - val_loss: 2.3790
Epoch 4/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1103 - loss: 2.9341 
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Epoch 5/110

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[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1361 - loss: 2.8022
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1362 - loss: 2.7999 - val_accuracy: 0.2202 - val_loss: 2.2887
Epoch 6/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1406 - loss: 2.7687
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1483 - loss: 2.7396 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1476 - loss: 2.7417
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[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1447 - loss: 2.7435
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1438 - loss: 2.7427
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1434 - loss: 2.7413
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1433 - loss: 2.7398
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1433 - loss: 2.7388 - val_accuracy: 0.2192 - val_loss: 2.2596
Epoch 7/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1719 - loss: 2.6541
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1336 - loss: 2.6985 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1408 - loss: 2.6900
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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1444 - loss: 2.6824
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1457 - loss: 2.6782
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1464 - loss: 2.6755
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1472 - loss: 2.6724
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1476 - loss: 2.6704 - val_accuracy: 0.2289 - val_loss: 2.2198
Epoch 8/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 27ms/step - accuracy: 0.2031 - loss: 2.7666
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1559 - loss: 2.6104 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1562 - loss: 2.6005
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1558 - loss: 2.5960
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1558 - loss: 2.5955
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1560 - loss: 2.5946
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1565 - loss: 2.5930
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Epoch 9/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1562 - loss: 2.5217
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1629 - loss: 2.5721 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1646 - loss: 2.5694
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1654 - loss: 2.5655
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[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1665 - loss: 2.5545
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Epoch 10/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1406 - loss: 2.4443
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1598 - loss: 2.5163 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1638 - loss: 2.5108
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[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1693 - loss: 2.4997
[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1702 - loss: 2.4963
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1706 - loss: 2.4931
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1711 - loss: 2.4895
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1716 - loss: 2.4870 - val_accuracy: 0.2381 - val_loss: 2.1033
Epoch 11/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.1562 - loss: 2.3502
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1860 - loss: 2.4081 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1848 - loss: 2.4110
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1835 - loss: 2.4132
[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1826 - loss: 2.4151
[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1819 - loss: 2.4178
[1m222/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1819 - loss: 2.4186
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1822 - loss: 2.4181
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1824 - loss: 2.4174 - val_accuracy: 0.2585 - val_loss: 2.0725
Epoch 12/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1719 - loss: 2.5292
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1983 - loss: 2.3895 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1969 - loss: 2.3861
[1m107/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1960 - loss: 2.3846
[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1957 - loss: 2.3829
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1955 - loss: 2.3819
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1949 - loss: 2.3812
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1943 - loss: 2.3800
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1940 - loss: 2.3793 - val_accuracy: 0.2494 - val_loss: 2.0340
Epoch 13/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1094 - loss: 2.5432
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1894 - loss: 2.3559 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1961 - loss: 2.3453
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1985 - loss: 2.3427
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1997 - loss: 2.3402
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2007 - loss: 2.3384
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2014 - loss: 2.3368
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2016 - loss: 2.3357
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2017 - loss: 2.3350 - val_accuracy: 0.2403 - val_loss: 2.0121
Epoch 14/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.2344 - loss: 2.2472
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2047 - loss: 2.3095 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2006 - loss: 2.3105
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[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1971 - loss: 2.3085
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1970 - loss: 2.3073
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1971 - loss: 2.3058
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1974 - loss: 2.3045
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1977 - loss: 2.3035 - val_accuracy: 0.2705 - val_loss: 1.9810
Epoch 15/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1562 - loss: 2.5097
[1m 31/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2000 - loss: 2.3233 
[1m 68/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2001 - loss: 2.2993
[1m108/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1997 - loss: 2.2881
[1m145/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2005 - loss: 2.2830
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2015 - loss: 2.2789
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2030 - loss: 2.2742 - val_accuracy: 0.2598 - val_loss: 1.9669
Epoch 16/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2188 - loss: 2.1436
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2024 - loss: 2.2043 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2036 - loss: 2.2233
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[1m161/292[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2054 - loss: 2.2354
[1m200/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2056 - loss: 2.2381
[1m239/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2060 - loss: 2.2392
[1m276/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2062 - loss: 2.2397
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2062 - loss: 2.2398 - val_accuracy: 0.2525 - val_loss: 1.9531
Epoch 17/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1875 - loss: 2.2861
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1962 - loss: 2.2573 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2016 - loss: 2.2436
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2045 - loss: 2.2351
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2072 - loss: 2.2302
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2089 - loss: 2.2281
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2102 - loss: 2.2266
[1m259/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2112 - loss: 2.2252
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2119 - loss: 2.2241 - val_accuracy: 0.2731 - val_loss: 1.9331
Epoch 18/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.1250 - loss: 2.3095
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2174 - loss: 2.1942 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2193 - loss: 2.1863
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2187 - loss: 2.1869
[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2187 - loss: 2.1876
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2186 - loss: 2.1880
[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2189 - loss: 2.1870
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2192 - loss: 2.1864
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2193 - loss: 2.1862 - val_accuracy: 0.2916 - val_loss: 1.9200
Epoch 19/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2656 - loss: 2.1561
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2443 - loss: 2.1528 
[1m 67/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2383 - loss: 2.1584
[1m106/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2351 - loss: 2.1577
[1m141/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2337 - loss: 2.1564
[1m180/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2326 - loss: 2.1564
[1m216/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2317 - loss: 2.1571
[1m256/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2308 - loss: 2.1582
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Epoch 20/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2812 - loss: 1.9817
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2160 - loss: 2.1418 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2163 - loss: 2.1424
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2209 - loss: 2.1436
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2218 - loss: 2.1435 - val_accuracy: 0.2840 - val_loss: 1.8870
Epoch 21/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2344 - loss: 2.0182
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2356 - loss: 2.1258 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2311 - loss: 2.1371
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2296 - loss: 2.1379
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2294 - loss: 2.1368
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2293 - loss: 2.1364
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2293 - loss: 2.1359
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2294 - loss: 2.1352
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2296 - loss: 2.1347 - val_accuracy: 0.2944 - val_loss: 1.8737
Epoch 22/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2812 - loss: 2.0360
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2297 - loss: 2.1323 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2332 - loss: 2.1238
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2340 - loss: 2.1197
[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2341 - loss: 2.1166
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2347 - loss: 2.1155
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2352 - loss: 2.1151
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2354 - loss: 2.1147
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2353 - loss: 2.1148 - val_accuracy: 0.2945 - val_loss: 1.8606
Epoch 23/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1875 - loss: 2.0249
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2307 - loss: 2.0658 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2305 - loss: 2.0790
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2322 - loss: 2.0845
[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2344 - loss: 2.0876
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2361 - loss: 2.0897
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2369 - loss: 2.0912
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2378 - loss: 2.0921
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2382 - loss: 2.0928 - val_accuracy: 0.3021 - val_loss: 1.8526
Epoch 24/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 25ms/step - accuracy: 0.3594 - loss: 1.8542
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2780 - loss: 2.0210 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2689 - loss: 2.0444
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2633 - loss: 2.0539
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2600 - loss: 2.0580
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2583 - loss: 2.0601
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2565 - loss: 2.0624
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2551 - loss: 2.0643
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2544 - loss: 2.0652 - val_accuracy: 0.3008 - val_loss: 1.8454
Epoch 25/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2031 - loss: 2.2031
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2351 - loss: 2.0972 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2405 - loss: 2.0884
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[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2429 - loss: 2.0844
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2432 - loss: 2.0835
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2434 - loss: 2.0827
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2439 - loss: 2.0811
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2443 - loss: 2.0800 - val_accuracy: 0.3032 - val_loss: 1.8407
Epoch 26/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2344 - loss: 2.1277
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2390 - loss: 2.0896 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2413 - loss: 2.0804
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2441 - loss: 2.0743
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2462 - loss: 2.0689
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2477 - loss: 2.0659
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2488 - loss: 2.0642
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2495 - loss: 2.0631
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2498 - loss: 2.0624 - val_accuracy: 0.3043 - val_loss: 1.8240
Epoch 27/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2031 - loss: 1.9873
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2588 - loss: 2.0288 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2574 - loss: 2.0365
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2567 - loss: 2.0416
[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2563 - loss: 2.0442
[1m197/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2560 - loss: 2.0454
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2558 - loss: 2.0458
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2557 - loss: 2.0460
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2556 - loss: 2.0460 - val_accuracy: 0.3045 - val_loss: 1.8192
Epoch 28/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.3125 - loss: 1.8525
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2762 - loss: 1.9929 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2713 - loss: 2.0047
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2686 - loss: 2.0134
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2667 - loss: 2.0186
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2655 - loss: 2.0221
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2648 - loss: 2.0239
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2642 - loss: 2.0249
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2639 - loss: 2.0255 - val_accuracy: 0.3184 - val_loss: 1.8145
Epoch 29/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.2812 - loss: 1.8982
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2745 - loss: 1.9885 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2703 - loss: 2.0067
[1m111/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2661 - loss: 2.0150
[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2639 - loss: 2.0187
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2629 - loss: 2.0197
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2622 - loss: 2.0206
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2616 - loss: 2.0222
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2614 - loss: 2.0229 - val_accuracy: 0.3105 - val_loss: 1.8132
Epoch 30/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2031 - loss: 2.1626
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2480 - loss: 2.0438 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2489 - loss: 2.0402
[1m121/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2526 - loss: 2.0320
[1m162/292[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2550 - loss: 2.0267
[1m199/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2563 - loss: 2.0237
[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2570 - loss: 2.0217
[1m275/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2577 - loss: 2.0200
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Epoch 31/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2504 - loss: 2.0541 
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Epoch 32/110

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Epoch 33/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2719 - loss: 1.9591 
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[1m240/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2774 - loss: 1.9630
[1m279/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2774 - loss: 1.9642
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2774 - loss: 1.9647 - val_accuracy: 0.3362 - val_loss: 1.7682
Epoch 34/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2876 - loss: 1.9857 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2850 - loss: 1.9770
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2802 - loss: 1.9738
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2800 - loss: 1.9729
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2799 - loss: 1.9723
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2798 - loss: 1.9723
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2796 - loss: 1.9726 - val_accuracy: 0.3293 - val_loss: 1.7587
Epoch 35/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2812 - loss: 1.9054
[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2765 - loss: 1.9464 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2754 - loss: 1.9593
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2777 - loss: 1.9692
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2786 - loss: 1.9689
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Epoch 36/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3022 - loss: 1.9260 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2946 - loss: 1.9396
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2921 - loss: 1.9464
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2912 - loss: 1.9477 - val_accuracy: 0.3197 - val_loss: 1.7447
Epoch 37/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2812 - loss: 1.9836
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2972 - loss: 1.9414 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2932 - loss: 1.9448
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2896 - loss: 1.9475
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2893 - loss: 1.9486 - val_accuracy: 0.3338 - val_loss: 1.7418
Epoch 38/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.2344 - loss: 1.9394
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2894 - loss: 1.9554 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2886 - loss: 1.9615
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[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2878 - loss: 1.9565
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2878 - loss: 1.9551
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2879 - loss: 1.9544 - val_accuracy: 0.3336 - val_loss: 1.7284
Epoch 39/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.4219 - loss: 1.9135
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3018 - loss: 1.9477 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2979 - loss: 1.9350
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[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2966 - loss: 1.9286
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2959 - loss: 1.9277
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2952 - loss: 1.9280
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2949 - loss: 1.9282
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2949 - loss: 1.9284 - val_accuracy: 0.3288 - val_loss: 1.7245
Epoch 40/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.3594 - loss: 1.8040
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2993 - loss: 1.8851 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2980 - loss: 1.9013
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2970 - loss: 1.9120
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2965 - loss: 1.9162
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2956 - loss: 1.9185
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2949 - loss: 1.9206
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2943 - loss: 1.9224
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2940 - loss: 1.9232 - val_accuracy: 0.3378 - val_loss: 1.7254
Epoch 41/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2188 - loss: 2.0847
[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2815 - loss: 1.9218 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2844 - loss: 1.9159
[1m123/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2869 - loss: 1.9160
[1m164/292[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2887 - loss: 1.9158
[1m202/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2902 - loss: 1.9149
[1m237/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2912 - loss: 1.9144
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2919 - loss: 1.9143
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Epoch 42/110

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Epoch 43/110

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Epoch 44/110

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[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3070 - loss: 1.9141
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3065 - loss: 1.9123 - val_accuracy: 0.3436 - val_loss: 1.6946
Epoch 45/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3045 - loss: 1.9329 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3053 - loss: 1.9141
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[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3055 - loss: 1.9001
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3058 - loss: 1.8980
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3061 - loss: 1.8966
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3063 - loss: 1.8959 - val_accuracy: 0.3547 - val_loss: 1.6850
Epoch 46/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3140 - loss: 1.9096 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3164 - loss: 1.8990
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3118 - loss: 1.8959
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Epoch 47/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2999 - loss: 1.9034 
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Epoch 48/110

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Epoch 49/110

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Epoch 50/110

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[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3157 - loss: 1.8593
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[1m182/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3180 - loss: 1.8626
[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3180 - loss: 1.8634
[1m257/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3184 - loss: 1.8634
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3186 - loss: 1.8631 - val_accuracy: 0.3600 - val_loss: 1.6635
Epoch 51/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3283 - loss: 1.8390 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3250 - loss: 1.8535
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3238 - loss: 1.8567
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3233 - loss: 1.8567
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3231 - loss: 1.8559
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3231 - loss: 1.8550
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Epoch 52/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3408 - loss: 1.8441 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3347 - loss: 1.8548
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Epoch 53/110

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Epoch 54/110

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Epoch 55/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3370 - loss: 1.8521 
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3342 - loss: 1.8288
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3330 - loss: 1.8295 - val_accuracy: 0.3558 - val_loss: 1.6371
Epoch 56/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3472 - loss: 1.8035 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3459 - loss: 1.7999
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[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3407 - loss: 1.8058
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3386 - loss: 1.8096
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3372 - loss: 1.8122
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3363 - loss: 1.8137
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3358 - loss: 1.8146 - val_accuracy: 0.3547 - val_loss: 1.6331
Epoch 57/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3374 - loss: 1.8543 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3295 - loss: 1.8482
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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3275 - loss: 1.8269
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Epoch 58/110

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Epoch 59/110

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Epoch 60/110

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Epoch 61/110

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Epoch 62/110

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Epoch 63/110

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Epoch 64/110

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Epoch 65/110

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Epoch 66/110

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Epoch 67/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3659 - loss: 1.7248 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3574 - loss: 1.7431
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3521 - loss: 1.7579
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Epoch 68/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3306 - loss: 1.7332 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3326 - loss: 1.7471
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Epoch 69/110

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Epoch 70/110

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Epoch 71/110

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Epoch 72/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3688 - loss: 1.7446 
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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3486 - loss: 1.7644
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3479 - loss: 1.7660
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3477 - loss: 1.7665 - val_accuracy: 0.3560 - val_loss: 1.6071
Epoch 73/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3444 - loss: 1.7246 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3448 - loss: 1.7410
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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3459 - loss: 1.7545
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3460 - loss: 1.7552
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Epoch 74/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3526 - loss: 1.7682 
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[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3486 - loss: 1.7485
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Epoch 75/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3345 - loss: 1.7634 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3371 - loss: 1.7634
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3392 - loss: 1.7643
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Epoch 76/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3407 - loss: 1.7517 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3399 - loss: 1.7561
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[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3415 - loss: 1.7546
[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3423 - loss: 1.7544
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3434 - loss: 1.7535
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3451 - loss: 1.7520 - val_accuracy: 0.3730 - val_loss: 1.5946
Epoch 77/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2812 - loss: 1.7210
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3348 - loss: 1.7639 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3415 - loss: 1.7570
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3433 - loss: 1.7558
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3441 - loss: 1.7572
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3442 - loss: 1.7580
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3444 - loss: 1.7586
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3449 - loss: 1.7576
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3452 - loss: 1.7570 - val_accuracy: 0.3741 - val_loss: 1.6018
Epoch 78/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3906 - loss: 1.6968
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3516 - loss: 1.7209 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3515 - loss: 1.7230
[1m119/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3511 - loss: 1.7269
[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3497 - loss: 1.7311
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3486 - loss: 1.7341
[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3479 - loss: 1.7361
[1m275/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3476 - loss: 1.7366
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3475 - loss: 1.7369 - val_accuracy: 0.3632 - val_loss: 1.6006
Epoch 79/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4062 - loss: 1.6010
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3624 - loss: 1.7188 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3589 - loss: 1.7342
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3577 - loss: 1.7387
[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3558 - loss: 1.7411
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3547 - loss: 1.7425
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3539 - loss: 1.7436
[1m262/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3531 - loss: 1.7441
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3525 - loss: 1.7444 - val_accuracy: 0.3608 - val_loss: 1.6167
Epoch 80/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3348 - loss: 1.7901 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3407 - loss: 1.7710
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3437 - loss: 1.7613
[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3460 - loss: 1.7524
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Epoch 81/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3421 - loss: 1.7294 
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[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3461 - loss: 1.7296
[1m262/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3465 - loss: 1.7307
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3467 - loss: 1.7317 - val_accuracy: 0.3687 - val_loss: 1.5957

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[1m57/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 911us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
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Saved model to disk.

Accuracy capturado en la ejecución 7: 36.32 [%]
F1-score capturado en la ejecución 7: 35.01 [%]

=== EJECUCIÓN 8 ===

--- TRAIN (ejecución 8) ---

--- TEST (ejecución 8) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18676, 6, 250)

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[1m 68/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 751us/step  
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[1m297/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 681us/step
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[1m446/584[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 679us/step
[1m520/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 679us/step
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

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[1m70/86[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 727us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 8ms/step

[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 70/169[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 732us/step
[1m145/169[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 702us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 36.87 [%]
Global F1 score (validation) = 35.57 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[2.1758869e-01 1.8739465e-01 2.1209347e-01 ... 2.5686900e-06
  1.4959301e-01 2.3017341e-02]
 [2.1554945e-01 2.0356999e-01 2.2214764e-01 ... 4.3660689e-06
  1.3480118e-01 1.0707350e-02]
 [1.8041098e-01 1.9215713e-01 1.8842952e-01 ... 2.4403733e-05
  2.0530243e-01 4.0281035e-02]
 ...
 [1.9045095e-01 1.9601682e-01 1.9501571e-01 ... 4.4898312e-05
  1.9145802e-01 2.4026854e-02]
 [1.7201932e-01 2.0647097e-01 1.8431370e-01 ... 1.9744076e-04
  1.7169361e-01 7.8507345e-03]
 [7.0252568e-02 9.4359629e-02 7.8760989e-02 ... 1.9095500e-03
  1.1761836e-01 4.9799532e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 43.84 [%]
Global accuracy score (test) = 35.15 [%]
Global F1 score (train) = 42.29 [%]
Global F1 score (test) = 33.73 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.21      0.46      0.29       184
 CAMINAR CON MÓVIL O LIBRO       0.17      0.20      0.18       184
       CAMINAR USUAL SPEED       0.22      0.25      0.24       184
            CAMINAR ZIGZAG       0.00      0.00      0.00       184
          DE PIE BARRIENDO       0.21      0.30      0.25       184
   DE PIE DOBLANDO TOALLAS       0.25      0.10      0.15       184
    DE PIE MOVIENDO LIBROS       0.27      0.35      0.31       184
          DE PIE USANDO PC       0.39      0.57      0.46       184
        FASE REPOSO CON K5       0.53      0.84      0.65       184
INCREMENTAL CICLOERGOMETRO       0.90      0.61      0.72       184
           SENTADO LEYENDO       0.33      0.38      0.35       184
         SENTADO USANDO PC       0.24      0.11      0.15       184
      SENTADO VIENDO LA TV       0.38      0.31      0.34       184
   SUBIR Y BAJAR ESCALERAS       0.38      0.15      0.21       184
                    TROTAR       0.82      0.70      0.76       161

                  accuracy                           0.35      2737
                 macro avg       0.35      0.35      0.34      2737
              weighted avg       0.35      0.35      0.33      2737

2025-10-28 14:09:59.057322: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 14:09:59.068479: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1761656999.081568 2268254 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761656999.085777 2268254 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761656999.095676 2268254 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761656999.095697 2268254 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761656999.095699 2268254 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761656999.095702 2268254 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 14:09:59.098762: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1761657001.464961 2268254 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13697 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/110
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761657003.186548 2268367 service.cc:152] XLA service 0x7c09b400ddb0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761657003.186584 2268367 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 14:10:03.220068: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
I0000 00:00:1761657003.392645 2268367 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761657005.691843 2268367 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/110

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Epoch 3/110

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Epoch 4/110

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Epoch 5/110

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Epoch 6/110

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Epoch 7/110

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[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1448 - loss: 2.7005
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1456 - loss: 2.6967 - val_accuracy: 0.2189 - val_loss: 2.2639
Epoch 8/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1472 - loss: 2.6690 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1494 - loss: 2.6572
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1516 - loss: 2.6468
[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1514 - loss: 2.6417
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1516 - loss: 2.6373
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1523 - loss: 2.6331
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1528 - loss: 2.6298
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1532 - loss: 2.6278 - val_accuracy: 0.2155 - val_loss: 2.2287
Epoch 9/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1542 - loss: 2.6060 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1568 - loss: 2.5880
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1583 - loss: 2.5791
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1593 - loss: 2.5735
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1603 - loss: 2.5701
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1613 - loss: 2.5668
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1622 - loss: 2.5644
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1626 - loss: 2.5633 - val_accuracy: 0.2435 - val_loss: 2.1925
Epoch 10/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1617 - loss: 2.5019 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1624 - loss: 2.5153
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1693 - loss: 2.5074 - val_accuracy: 0.2494 - val_loss: 2.1479
Epoch 11/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1719 - loss: 2.4274
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1643 - loss: 2.4904 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1698 - loss: 2.4760
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1735 - loss: 2.4677
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1755 - loss: 2.4584
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Epoch 12/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1562 - loss: 2.3943
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1857 - loss: 2.4349 
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[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1863 - loss: 2.4152
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1863 - loss: 2.4133
[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1862 - loss: 2.4117
[1m258/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1860 - loss: 2.4102
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1858 - loss: 2.4090 - val_accuracy: 0.2416 - val_loss: 2.0941
Epoch 13/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1562 - loss: 2.4914
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1698 - loss: 2.4093 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1748 - loss: 2.4031
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1776 - loss: 2.3938
[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1796 - loss: 2.3860
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1805 - loss: 2.3822
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1814 - loss: 2.3792
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1823 - loss: 2.3767
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1828 - loss: 2.3753 - val_accuracy: 0.2544 - val_loss: 2.0524
Epoch 14/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1875 - loss: 2.4078
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2012 - loss: 2.3076 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2019 - loss: 2.3106
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2007 - loss: 2.3117
[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1995 - loss: 2.3141
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1989 - loss: 2.3156
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1988 - loss: 2.3157
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1988 - loss: 2.3157
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1988 - loss: 2.3157 - val_accuracy: 0.2838 - val_loss: 2.0279
Epoch 15/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2031 - loss: 2.3121
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1988 - loss: 2.2886 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1969 - loss: 2.2921
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1962 - loss: 2.2962
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1962 - loss: 2.2966
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1959 - loss: 2.2964
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1956 - loss: 2.2957
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1954 - loss: 2.2949
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1953 - loss: 2.2945 - val_accuracy: 0.2686 - val_loss: 1.9937
Epoch 16/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1719 - loss: 2.3447
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2001 - loss: 2.2818 
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Epoch 17/110

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Epoch 18/110

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[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2329 - loss: 2.1778
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2277 - loss: 2.1875
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2272 - loss: 2.1888
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2268 - loss: 2.1896 - val_accuracy: 0.2934 - val_loss: 1.9339
Epoch 19/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.2031 - loss: 2.1254
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2029 - loss: 2.1806 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2105 - loss: 2.1693
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[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2143 - loss: 2.1676
[1m197/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2150 - loss: 2.1691
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2156 - loss: 2.1695
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2159 - loss: 2.1698
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2161 - loss: 2.1700 - val_accuracy: 0.2997 - val_loss: 1.9090
Epoch 20/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2173 - loss: 2.1577 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2186 - loss: 2.1572
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2205 - loss: 2.1571
[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2220 - loss: 2.1566
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2227 - loss: 2.1571
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2231 - loss: 2.1575
[1m259/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2232 - loss: 2.1577
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2234 - loss: 2.1579 - val_accuracy: 0.3062 - val_loss: 1.8908
Epoch 21/110

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[1m 34/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2362 - loss: 2.1795 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2319 - loss: 2.1686
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2296 - loss: 2.1527 - val_accuracy: 0.2947 - val_loss: 1.8854
Epoch 22/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2812 - loss: 2.0494
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2455 - loss: 2.1182 
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Epoch 23/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.2656 - loss: 2.3105
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2438 - loss: 2.0944 
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[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2369 - loss: 2.0979
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2364 - loss: 2.0997
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2360 - loss: 2.1005 - val_accuracy: 0.3080 - val_loss: 1.8570
Epoch 24/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 25ms/step - accuracy: 0.2500 - loss: 2.1506
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2489 - loss: 2.0760 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2459 - loss: 2.0786
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[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2439 - loss: 2.0815
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2430 - loss: 2.0836
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2426 - loss: 2.0848
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2424 - loss: 2.0856
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2424 - loss: 2.0859 - val_accuracy: 0.3175 - val_loss: 1.8482
Epoch 25/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.1875 - loss: 2.1990
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2418 - loss: 2.0860 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2433 - loss: 2.0876
[1m109/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2437 - loss: 2.0863
[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2439 - loss: 2.0846
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2439 - loss: 2.0830
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2443 - loss: 2.0817
[1m263/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2444 - loss: 2.0808
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2445 - loss: 2.0802 - val_accuracy: 0.3108 - val_loss: 1.8337
Epoch 26/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2031 - loss: 2.0459
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2531 - loss: 2.0590 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2512 - loss: 2.0628
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[1m146/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2501 - loss: 2.0669
[1m182/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2494 - loss: 2.0681
[1m219/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2487 - loss: 2.0685
[1m255/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2481 - loss: 2.0686
[1m291/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2478 - loss: 2.0684
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Epoch 27/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2611 - loss: 2.0237 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2618 - loss: 2.0389
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[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2605 - loss: 2.0491
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2596 - loss: 2.0493
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2590 - loss: 2.0501
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2584 - loss: 2.0507 - val_accuracy: 0.3216 - val_loss: 1.8188
Epoch 28/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2454 - loss: 2.0700 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2465 - loss: 2.0613
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[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2487 - loss: 2.0543
[1m275/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2492 - loss: 2.0533
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2495 - loss: 2.0527 - val_accuracy: 0.3116 - val_loss: 1.8076
Epoch 29/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2588 - loss: 2.0428 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2618 - loss: 2.0461
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2612 - loss: 2.0439
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2603 - loss: 2.0435
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2594 - loss: 2.0434
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2587 - loss: 2.0428
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2584 - loss: 2.0422 - val_accuracy: 0.3249 - val_loss: 1.8025
Epoch 30/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2601 - loss: 2.0179 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2576 - loss: 2.0236
[1m111/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2566 - loss: 2.0245
[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2567 - loss: 2.0240
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2574 - loss: 2.0245
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2578 - loss: 2.0249
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2582 - loss: 2.0248
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2584 - loss: 2.0248 - val_accuracy: 0.3291 - val_loss: 1.7931
Epoch 31/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2408 - loss: 2.0369 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2500 - loss: 2.0224
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[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2586 - loss: 2.0159
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2606 - loss: 2.0139
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2617 - loss: 2.0135
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2622 - loss: 2.0134
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2624 - loss: 2.0134 - val_accuracy: 0.3282 - val_loss: 1.7882
Epoch 32/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2767 - loss: 1.9642 
[1m 83/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2711 - loss: 1.9814
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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2644 - loss: 1.9973
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Epoch 33/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3594 - loss: 2.2012
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2620 - loss: 2.0450 
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[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2683 - loss: 2.0210
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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2713 - loss: 2.0129
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Epoch 34/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.2500 - loss: 1.9385
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2766 - loss: 2.0035 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2802 - loss: 1.9964
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[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2806 - loss: 1.9830
[1m199/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2797 - loss: 1.9814
[1m240/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2789 - loss: 1.9812
[1m280/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2785 - loss: 1.9811
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2783 - loss: 1.9811 - val_accuracy: 0.3258 - val_loss: 1.7691
Epoch 35/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.3438 - loss: 1.8709
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2778 - loss: 1.9839 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2789 - loss: 1.9836
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2804 - loss: 1.9814
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2803 - loss: 1.9793
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2799 - loss: 1.9782
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2797 - loss: 1.9776
[1m275/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2797 - loss: 1.9769
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2797 - loss: 1.9767 - val_accuracy: 0.3365 - val_loss: 1.7693
Epoch 36/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3125 - loss: 2.0226
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2980 - loss: 1.9340 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2903 - loss: 1.9502
[1m119/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2857 - loss: 1.9564
[1m160/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2831 - loss: 1.9593
[1m200/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2815 - loss: 1.9613
[1m241/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2802 - loss: 1.9634
[1m278/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2793 - loss: 1.9652
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2791 - loss: 1.9657 - val_accuracy: 0.3336 - val_loss: 1.7689
Epoch 37/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3438 - loss: 1.8790
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2862 - loss: 1.9517 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2811 - loss: 1.9604
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[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2784 - loss: 1.9665
[1m197/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2779 - loss: 1.9665
[1m237/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2779 - loss: 1.9667
[1m276/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2781 - loss: 1.9667
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Epoch 38/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2733 - loss: 1.9430 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2778 - loss: 1.9403
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Epoch 39/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3119 - loss: 1.9365 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3053 - loss: 1.9497
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2924 - loss: 1.9562
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Epoch 40/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2811 - loss: 1.9275 
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[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2865 - loss: 1.9387
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2870 - loss: 1.9389
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2873 - loss: 1.9389 - val_accuracy: 0.3599 - val_loss: 1.7299
Epoch 41/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2914 - loss: 1.9146 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2915 - loss: 1.9252
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2913 - loss: 1.9268
[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2919 - loss: 1.9254
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2918 - loss: 1.9257
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2918 - loss: 1.9261
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2917 - loss: 1.9264
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2919 - loss: 1.9261 - val_accuracy: 0.3404 - val_loss: 1.7196
Epoch 42/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2880 - loss: 1.9474 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2896 - loss: 1.9364
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[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2912 - loss: 1.9317
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2920 - loss: 1.9306
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2923 - loss: 1.9304
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2925 - loss: 1.9299
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2928 - loss: 1.9298 - val_accuracy: 0.3462 - val_loss: 1.7154
Epoch 43/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3015 - loss: 1.8866 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3020 - loss: 1.8881
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Epoch 44/110

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Epoch 45/110

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Epoch 46/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3040 - loss: 1.8958 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3052 - loss: 1.8916
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[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3054 - loss: 1.8961
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3053 - loss: 1.8964
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3051 - loss: 1.8963
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3051 - loss: 1.8962 - val_accuracy: 0.3500 - val_loss: 1.7015
Epoch 47/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2812 - loss: 1.8154
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3250 - loss: 1.8359 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3190 - loss: 1.8596
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[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3142 - loss: 1.8755
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3130 - loss: 1.8788
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3116 - loss: 1.8810
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3108 - loss: 1.8820
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3104 - loss: 1.8827 - val_accuracy: 0.3517 - val_loss: 1.7002
Epoch 48/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2823 - loss: 1.9052 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2894 - loss: 1.9035
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[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2967 - loss: 1.8923
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2979 - loss: 1.8911
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2991 - loss: 1.8900
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Epoch 49/110

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Epoch 50/110

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Epoch 51/110

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Epoch 52/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3103 - loss: 1.8468 
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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3133 - loss: 1.8608
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3135 - loss: 1.8618
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3135 - loss: 1.8623 - val_accuracy: 0.3502 - val_loss: 1.6726
Epoch 53/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3113 - loss: 1.8616 
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[1m240/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3132 - loss: 1.8598
[1m279/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3131 - loss: 1.8595
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Epoch 54/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3411 - loss: 1.8219 
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Epoch 55/110

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Epoch 56/110

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Epoch 57/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3024 - loss: 1.8534 
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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3175 - loss: 1.8417
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3186 - loss: 1.8400
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3192 - loss: 1.8391 - val_accuracy: 0.3582 - val_loss: 1.6483
Epoch 58/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3321 - loss: 1.8128 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3293 - loss: 1.8149
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[1m198/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3279 - loss: 1.8213
[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3272 - loss: 1.8235
[1m279/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3267 - loss: 1.8248
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3265 - loss: 1.8251 - val_accuracy: 0.3652 - val_loss: 1.6525
Epoch 59/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3095 - loss: 1.8346 
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3201 - loss: 1.8190
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Epoch 60/110

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Epoch 61/110

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Epoch 62/110

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Epoch 63/110

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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3425 - loss: 1.7997
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Epoch 64/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3319 - loss: 1.7874 
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Epoch 65/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3208 - loss: 1.7905 
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3308 - loss: 1.7992
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3312 - loss: 1.8007
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3314 - loss: 1.8013 - val_accuracy: 0.3578 - val_loss: 1.6362

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:10[0m 834ms/step
[1m62/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 829us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
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Saved model to disk.

Accuracy capturado en la ejecución 8: 35.15 [%]
F1-score capturado en la ejecución 8: 33.73 [%]

=== EJECUCIÓN 9 ===

--- TRAIN (ejecución 9) ---

--- TEST (ejecución 9) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18676, 6, 250)

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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
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[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 8ms/step

[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 15ms/step
[1m 64/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 802us/step
[1m134/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 756us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 35.78 [%]
Global F1 score (validation) = 35.13 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[1.96622655e-01 1.94313973e-01 1.93572938e-01 ... 9.70064775e-06
  1.62673101e-01 1.94760934e-02]
 [1.94277629e-01 1.87786192e-01 1.97243825e-01 ... 4.31899061e-05
  1.57837868e-01 3.06016374e-02]
 [1.92171320e-01 1.70958623e-01 1.99996620e-01 ... 1.94068725e-05
  1.50243789e-01 8.19846839e-02]
 ...
 [1.95692420e-01 1.98974699e-01 1.99989721e-01 ... 1.56727125e-04
  1.50935084e-01 4.08139713e-02]
 [2.06660047e-01 2.10209742e-01 1.98801383e-01 ... 3.65345986e-05
  1.45613372e-01 2.84206532e-02]
 [1.11988239e-01 1.41234934e-01 1.15392692e-01 ... 1.35842105e-03
  1.12511516e-01 7.62731442e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 43.05 [%]
Global accuracy score (test) = 36.65 [%]
Global F1 score (train) = 41.67 [%]
Global F1 score (test) = 35.92 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.35      0.16      0.22       184
 CAMINAR CON MÓVIL O LIBRO       0.26      0.26      0.26       184
       CAMINAR USUAL SPEED       0.28      0.14      0.18       184
            CAMINAR ZIGZAG       0.21      0.51      0.30       184
          DE PIE BARRIENDO       0.21      0.41      0.27       184
   DE PIE DOBLANDO TOALLAS       0.34      0.24      0.28       184
    DE PIE MOVIENDO LIBROS       0.26      0.25      0.26       184
          DE PIE USANDO PC       0.41      0.56      0.47       184
        FASE REPOSO CON K5       0.56      0.82      0.67       184
INCREMENTAL CICLOERGOMETRO       0.97      0.58      0.73       184
           SENTADO LEYENDO       0.32      0.39      0.35       184
         SENTADO USANDO PC       0.23      0.11      0.15       184
      SENTADO VIENDO LA TV       0.40      0.36      0.38       184
   SUBIR Y BAJAR ESCALERAS       0.45      0.07      0.12       184
                    TROTAR       0.81      0.70      0.75       161

                  accuracy                           0.37      2737
                 macro avg       0.40      0.37      0.36      2737
              weighted avg       0.40      0.37      0.36      2737

2025-10-28 14:10:58.254622: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 14:10:58.265830: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1761657058.278904 2275303 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761657058.283147 2275303 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761657058.293769 2275303 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657058.293791 2275303 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657058.293794 2275303 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657058.293796 2275303 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 14:10:58.296946: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1761657060.672156 2275303 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13697 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/110
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761657062.398172 2275433 service.cc:152] XLA service 0x76bec800c820 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761657062.398224 2275433 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 14:11:02.432528: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
I0000 00:00:1761657062.603361 2275433 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761657064.851810 2275433 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16:39[0m 3s/step - accuracy: 0.0469 - loss: 3.8360
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[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.0750 - loss: 3.5108
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Epoch 2/110

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Epoch 3/110

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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1160 - loss: 3.0658
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1165 - loss: 3.0618 - val_accuracy: 0.1796 - val_loss: 2.3797
Epoch 4/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1282 - loss: 2.9177 
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[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1281 - loss: 2.9309
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1280 - loss: 2.9309
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1280 - loss: 2.9305 - val_accuracy: 0.1843 - val_loss: 2.3506
Epoch 5/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1357 - loss: 2.8366 
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[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1347 - loss: 2.8461
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1344 - loss: 2.8462
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1342 - loss: 2.8450
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Epoch 6/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1349 - loss: 2.7844 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1389 - loss: 2.7762
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Epoch 7/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1530 - loss: 2.7258 
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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1526 - loss: 2.7034
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Epoch 8/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1641 - loss: 2.6551 
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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1587 - loss: 2.6364
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1588 - loss: 2.6315 - val_accuracy: 0.2196 - val_loss: 2.2186
Epoch 9/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1562 - loss: 2.6340
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1607 - loss: 2.5791 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1633 - loss: 2.5656
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[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1621 - loss: 2.5635
[1m198/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1614 - loss: 2.5632
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1611 - loss: 2.5626
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1611 - loss: 2.5613
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1612 - loss: 2.5605 - val_accuracy: 0.2183 - val_loss: 2.1855
Epoch 10/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.2031 - loss: 2.3686
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1781 - loss: 2.4842 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1721 - loss: 2.5047
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[1m160/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1717 - loss: 2.5084
[1m201/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1716 - loss: 2.5084
[1m239/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1714 - loss: 2.5079
[1m277/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1715 - loss: 2.5072
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1715 - loss: 2.5068 - val_accuracy: 0.2394 - val_loss: 2.1416
Epoch 11/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1616 - loss: 2.5188 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1658 - loss: 2.5008
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[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1708 - loss: 2.4834
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1716 - loss: 2.4807
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1725 - loss: 2.4772
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Epoch 12/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1777 - loss: 2.4237 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1762 - loss: 2.4248
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Epoch 13/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1748 - loss: 2.3961 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1782 - loss: 2.3950
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1829 - loss: 2.3846
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1839 - loss: 2.3816 - val_accuracy: 0.2531 - val_loss: 2.0521
Epoch 14/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1872 - loss: 2.3454 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1908 - loss: 2.3348
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[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1939 - loss: 2.3289
[1m237/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1945 - loss: 2.3284
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1950 - loss: 2.3278
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1951 - loss: 2.3275 - val_accuracy: 0.2694 - val_loss: 2.0236
Epoch 15/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2132 - loss: 2.2732 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2112 - loss: 2.2832
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2106 - loss: 2.2867
[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2114 - loss: 2.2864
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2113 - loss: 2.2870
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2106 - loss: 2.2885
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2100 - loss: 2.2894
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2096 - loss: 2.2897 - val_accuracy: 0.2809 - val_loss: 1.9999
Epoch 16/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2168 - loss: 2.2453 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2147 - loss: 2.2508
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[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2132 - loss: 2.2520
[1m197/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2122 - loss: 2.2526
[1m237/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2115 - loss: 2.2533
[1m277/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2110 - loss: 2.2538
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2109 - loss: 2.2537 - val_accuracy: 0.2764 - val_loss: 1.9849
Epoch 17/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1895 - loss: 2.2585 
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[1m216/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2038 - loss: 2.2477
[1m254/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2041 - loss: 2.2466
[1m288/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2045 - loss: 2.2453
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2046 - loss: 2.2450 - val_accuracy: 0.2686 - val_loss: 1.9519
Epoch 18/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2188 - loss: 2.0972
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2104 - loss: 2.1994 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2137 - loss: 2.2076
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2163 - loss: 2.2093
[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2171 - loss: 2.2103
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2177 - loss: 2.2103
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2180 - loss: 2.2098
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2184 - loss: 2.2093
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2189 - loss: 2.2086 - val_accuracy: 0.2831 - val_loss: 1.9317
Epoch 19/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3438 - loss: 1.8907
[1m 42/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2370 - loss: 2.1758 
[1m 82/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2260 - loss: 2.1915
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[1m161/292[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2229 - loss: 2.1956
[1m199/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2227 - loss: 2.1948
[1m239/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2228 - loss: 2.1937
[1m277/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2230 - loss: 2.1927
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2231 - loss: 2.1923 - val_accuracy: 0.2975 - val_loss: 1.9207
Epoch 20/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2188 - loss: 2.2043
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2148 - loss: 2.1984 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2180 - loss: 2.1852
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2201 - loss: 2.1791
[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2222 - loss: 2.1739
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2229 - loss: 2.1718
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2229 - loss: 2.1713
[1m262/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2230 - loss: 2.1707
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2233 - loss: 2.1700 - val_accuracy: 0.2810 - val_loss: 1.8989
Epoch 21/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2344 - loss: 2.1279
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2211 - loss: 2.1417 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2227 - loss: 2.1474
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2237 - loss: 2.1474
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2241 - loss: 2.1458
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2245 - loss: 2.1440
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2247 - loss: 2.1427
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2250 - loss: 2.1419
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2252 - loss: 2.1416 - val_accuracy: 0.3051 - val_loss: 1.8829
Epoch 22/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2812 - loss: 2.1543
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2203 - loss: 2.1661 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2310 - loss: 2.1500
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2346 - loss: 2.1422
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2365 - loss: 2.1356
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2376 - loss: 2.1307
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2378 - loss: 2.1277
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2379 - loss: 2.1259
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Epoch 23/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2669 - loss: 2.0769 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2556 - loss: 2.0921
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Epoch 24/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2344 - loss: 2.0638
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2518 - loss: 2.0860 
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[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2498 - loss: 2.0847
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2498 - loss: 2.0846 - val_accuracy: 0.3080 - val_loss: 1.8428
Epoch 25/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2188 - loss: 2.0754
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2296 - loss: 2.1054 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2359 - loss: 2.0966
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[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2416 - loss: 2.0883
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2429 - loss: 2.0862
[1m262/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2440 - loss: 2.0844
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2447 - loss: 2.0834 - val_accuracy: 0.3149 - val_loss: 1.8397
Epoch 26/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.1250 - loss: 2.2218
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2417 - loss: 2.0718 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2429 - loss: 2.0759
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2444 - loss: 2.0734
[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2456 - loss: 2.0706
[1m198/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2460 - loss: 2.0695
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2463 - loss: 2.0688
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2466 - loss: 2.0681
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2467 - loss: 2.0678 - val_accuracy: 0.3291 - val_loss: 1.8253
Epoch 27/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2500 - loss: 1.9227
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2456 - loss: 2.0364 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2516 - loss: 2.0341
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[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2559 - loss: 2.0347
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2576 - loss: 2.0335
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2582 - loss: 2.0335
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2587 - loss: 2.0342
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2589 - loss: 2.0346 - val_accuracy: 0.3204 - val_loss: 1.8005
Epoch 28/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2645 - loss: 2.0311 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2644 - loss: 2.0340
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[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2625 - loss: 2.0379
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2620 - loss: 2.0377
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2618 - loss: 2.0363
[1m275/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2616 - loss: 2.0352
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2616 - loss: 2.0349 - val_accuracy: 0.3314 - val_loss: 1.7974
Epoch 29/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2969 - loss: 1.8769
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2546 - loss: 2.0108 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2548 - loss: 2.0134
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2579 - loss: 2.0103
[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2594 - loss: 2.0109
[1m198/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2605 - loss: 2.0115
[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2612 - loss: 2.0120
[1m279/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2617 - loss: 2.0130
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2619 - loss: 2.0133 - val_accuracy: 0.3193 - val_loss: 1.7871
Epoch 30/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3594 - loss: 2.0836
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2729 - loss: 2.0124 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2695 - loss: 2.0079
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[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2649 - loss: 2.0142
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2648 - loss: 2.0151
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2649 - loss: 2.0153
[1m263/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2649 - loss: 2.0151
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2650 - loss: 2.0150 - val_accuracy: 0.3401 - val_loss: 1.7844
Epoch 31/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2969 - loss: 2.0258
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2711 - loss: 2.0646 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2757 - loss: 2.0309
[1m111/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2762 - loss: 2.0210
[1m144/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2749 - loss: 2.0188
[1m183/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2746 - loss: 2.0165
[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2744 - loss: 2.0150
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2743 - loss: 2.0136
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2744 - loss: 2.0123 - val_accuracy: 0.3365 - val_loss: 1.7816
Epoch 32/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.1562 - loss: 2.0081
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2676 - loss: 1.9649 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2767 - loss: 1.9650
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2778 - loss: 1.9687
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2780 - loss: 1.9712
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2781 - loss: 1.9728
[1m222/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2780 - loss: 1.9741
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2778 - loss: 1.9754
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2776 - loss: 1.9766 - val_accuracy: 0.3267 - val_loss: 1.7612
Epoch 33/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3438 - loss: 1.9446
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2813 - loss: 1.9761 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2811 - loss: 1.9885
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2817 - loss: 1.9869
[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2821 - loss: 1.9870
[1m201/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2820 - loss: 1.9872
[1m240/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2818 - loss: 1.9866
[1m278/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2819 - loss: 1.9862
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Epoch 34/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2965 - loss: 1.9126 
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Epoch 35/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2796 - loss: 1.9586 
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Epoch 36/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2888 - loss: 1.9545 
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2855 - loss: 1.9542
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2859 - loss: 1.9526
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Epoch 37/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2775 - loss: 1.9501 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2873 - loss: 1.9371
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[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2904 - loss: 1.9331
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2905 - loss: 1.9333
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2905 - loss: 1.9336 - val_accuracy: 0.3473 - val_loss: 1.7321
Epoch 38/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3072 - loss: 1.8999 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3018 - loss: 1.9193
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[1m218/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2964 - loss: 1.9366
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Epoch 39/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2977 - loss: 1.9545 
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Epoch 40/110

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Epoch 41/110

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Epoch 42/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3143 - loss: 1.9107 
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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3039 - loss: 1.9107
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3039 - loss: 1.9097 - val_accuracy: 0.3563 - val_loss: 1.6873
Epoch 43/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3241 - loss: 1.8977 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3140 - loss: 1.9024
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[1m254/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3096 - loss: 1.8942
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Epoch 44/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2947 - loss: 1.8940 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2989 - loss: 1.8909
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Epoch 45/110

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Epoch 46/110

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[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3172 - loss: 1.8749
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3158 - loss: 1.8763 - val_accuracy: 0.3549 - val_loss: 1.6719
Epoch 47/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.4375 - loss: 1.7864
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3244 - loss: 1.8711 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3199 - loss: 1.8706
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[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3182 - loss: 1.8703
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3177 - loss: 1.8698
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3169 - loss: 1.8691 - val_accuracy: 0.3567 - val_loss: 1.6666
Epoch 48/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3438 - loss: 2.0032
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3294 - loss: 1.8674 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3274 - loss: 1.8573
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[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3227 - loss: 1.8594
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3216 - loss: 1.8598
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3208 - loss: 1.8603
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3205 - loss: 1.8607
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3201 - loss: 1.8610 - val_accuracy: 0.3578 - val_loss: 1.6649
Epoch 49/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.4531 - loss: 1.6159
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3108 - loss: 1.8403 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3088 - loss: 1.8474
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3121 - loss: 1.8569
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3125 - loss: 1.8574
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Epoch 50/110

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Epoch 51/110

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Epoch 52/110

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Epoch 53/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3301 - loss: 1.8549 
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[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3273 - loss: 1.8308
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3268 - loss: 1.8307
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3266 - loss: 1.8308 - val_accuracy: 0.3650 - val_loss: 1.6449
Epoch 54/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3218 - loss: 1.8466 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3233 - loss: 1.8410
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[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3254 - loss: 1.8309
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3255 - loss: 1.8302
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3257 - loss: 1.8299
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Epoch 55/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3221 - loss: 1.8266 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3240 - loss: 1.8237
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Epoch 56/110

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Epoch 57/110

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Epoch 58/110

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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3307 - loss: 1.8127 - val_accuracy: 0.3441 - val_loss: 1.6388
Epoch 59/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3244 - loss: 1.8138 
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3256 - loss: 1.7998
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3254 - loss: 1.8007
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Epoch 60/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3502 - loss: 1.7597 
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[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3285 - loss: 1.8040
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Epoch 61/110

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Epoch 62/110

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Epoch 63/110

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Epoch 64/110

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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3328 - loss: 1.7733
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Epoch 65/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3330 - loss: 1.7845 
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3381 - loss: 1.7964
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Epoch 66/110

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[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3238 - loss: 1.7979
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Epoch 67/110

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Epoch 68/110

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Epoch 69/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3468 - loss: 1.7554 
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[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3431 - loss: 1.7726
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3432 - loss: 1.7727
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3431 - loss: 1.7732
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3431 - loss: 1.7732 - val_accuracy: 0.3722 - val_loss: 1.6103
Epoch 70/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3510 - loss: 1.7795 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3516 - loss: 1.7722
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3474 - loss: 1.7711
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3457 - loss: 1.7710
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3446 - loss: 1.7712
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3438 - loss: 1.7718
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Epoch 71/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3257 - loss: 1.8093 
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[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3295 - loss: 1.7904
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3302 - loss: 1.7883
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Epoch 72/110

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Epoch 73/110

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Epoch 74/110

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Epoch 75/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3439 - loss: 1.7847 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3355 - loss: 1.7927
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3347 - loss: 1.7810
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3352 - loss: 1.7799
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Epoch 76/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3544 - loss: 1.7016 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3473 - loss: 1.7253
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[1m202/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3449 - loss: 1.7433
[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3444 - loss: 1.7461
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Epoch 77/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3574 - loss: 1.7106 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3560 - loss: 1.7208
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Epoch 78/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3539 - loss: 1.7575 
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Epoch 79/110

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[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3396 - loss: 1.7473
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[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3400 - loss: 1.7465
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Epoch 80/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3386 - loss: 1.7028 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3370 - loss: 1.7206
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[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3352 - loss: 1.7455
[1m263/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3359 - loss: 1.7469
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3364 - loss: 1.7474 - val_accuracy: 0.3680 - val_loss: 1.6015
Epoch 81/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3527 - loss: 1.7592 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3518 - loss: 1.7560
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[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3488 - loss: 1.7494
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3478 - loss: 1.7480
[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3471 - loss: 1.7467
[1m277/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3465 - loss: 1.7461
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3463 - loss: 1.7461 - val_accuracy: 0.3682 - val_loss: 1.5969
Epoch 82/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3415 - loss: 1.7167 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3529 - loss: 1.7155
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[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3537 - loss: 1.7285
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Epoch 83/110

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Epoch 84/110

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Epoch 85/110

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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3473 - loss: 1.7357
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Epoch 86/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3470 - loss: 1.7673 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3442 - loss: 1.7565
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[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3463 - loss: 1.7451
[1m197/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3466 - loss: 1.7431
[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3470 - loss: 1.7414
[1m275/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3474 - loss: 1.7399
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3476 - loss: 1.7394 - val_accuracy: 0.3780 - val_loss: 1.6003
Epoch 87/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3565 - loss: 1.7618 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3476 - loss: 1.7513
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3473 - loss: 1.7480
[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3470 - loss: 1.7475
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3471 - loss: 1.7451
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3476 - loss: 1.7433
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Epoch 88/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3799 - loss: 1.7045 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3717 - loss: 1.7000
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Epoch 89/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3370 - loss: 1.7315 
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Epoch 90/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3493 - loss: 1.7320 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3536 - loss: 1.7212
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[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3533 - loss: 1.7229
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3532 - loss: 1.7221 - val_accuracy: 0.3706 - val_loss: 1.5950
Epoch 91/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.5000 - loss: 1.3908
[1m 42/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3511 - loss: 1.7398 
[1m 82/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3505 - loss: 1.7358
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[1m160/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3506 - loss: 1.7288
[1m201/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3512 - loss: 1.7255
[1m239/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3515 - loss: 1.7239
[1m276/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3516 - loss: 1.7233
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3517 - loss: 1.7230 - val_accuracy: 0.3808 - val_loss: 1.5978
Epoch 92/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3372 - loss: 1.7529 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3438 - loss: 1.7468
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3468 - loss: 1.7418
[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3488 - loss: 1.7388
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3498 - loss: 1.7369
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3506 - loss: 1.7342
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3512 - loss: 1.7323
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3514 - loss: 1.7316 - val_accuracy: 0.3763 - val_loss: 1.5934
Epoch 93/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3480 - loss: 1.7052 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3497 - loss: 1.7062
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3499 - loss: 1.7107
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3508 - loss: 1.7117
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3509 - loss: 1.7127
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3509 - loss: 1.7137
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3509 - loss: 1.7144
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3510 - loss: 1.7145 - val_accuracy: 0.3748 - val_loss: 1.5847
Epoch 94/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3281 - loss: 2.0157
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3439 - loss: 1.7909 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3491 - loss: 1.7568
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3508 - loss: 1.7422
[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3518 - loss: 1.7344
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3526 - loss: 1.7308
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3528 - loss: 1.7290
[1m259/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3529 - loss: 1.7271
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3531 - loss: 1.7252 - val_accuracy: 0.3700 - val_loss: 1.5889
Epoch 95/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.3125 - loss: 1.9645
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3486 - loss: 1.7227 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3516 - loss: 1.7104
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3527 - loss: 1.7068
[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3533 - loss: 1.7054
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3532 - loss: 1.7050
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3530 - loss: 1.7053
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3526 - loss: 1.7062 - val_accuracy: 0.3700 - val_loss: 1.5972
Epoch 96/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2969 - loss: 1.8515
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3512 - loss: 1.7484 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3571 - loss: 1.7296
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[1m160/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3568 - loss: 1.7256
[1m198/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3560 - loss: 1.7247
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3556 - loss: 1.7238
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3553 - loss: 1.7226
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3552 - loss: 1.7218 - val_accuracy: 0.3763 - val_loss: 1.5895
Epoch 97/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3438 - loss: 1.5365
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3729 - loss: 1.6744 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3663 - loss: 1.6906
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3645 - loss: 1.6946
[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3631 - loss: 1.6962
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3622 - loss: 1.6974
[1m237/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3618 - loss: 1.6980
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3613 - loss: 1.6988
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3611 - loss: 1.6995 - val_accuracy: 0.3813 - val_loss: 1.6012
Epoch 98/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3906 - loss: 1.5961
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3686 - loss: 1.6782 
[1m 69/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3701 - loss: 1.6785
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[1m142/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3647 - loss: 1.6861
[1m178/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3628 - loss: 1.6901
[1m218/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3610 - loss: 1.6940
[1m258/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3599 - loss: 1.6957
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3593 - loss: 1.6964 - val_accuracy: 0.3750 - val_loss: 1.5893

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:10[0m 830ms/step
[1m67/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 759us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 9ms/step
Saved model to disk.

Accuracy capturado en la ejecución 9: 36.65 [%]
F1-score capturado en la ejecución 9: 35.92 [%]

=== EJECUCIÓN 10 ===

--- TRAIN (ejecución 10) ---

--- TEST (ejecución 10) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18676, 6, 250)

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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m71/86[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 719us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step  
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[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 67/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 759us/step
[1m143/169[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 711us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 37.5 [%]
Global F1 score (validation) = 37.37 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[2.2775762e-01 1.9043878e-01 2.2327922e-01 ... 7.8588619e-07
  1.2197074e-01 1.7736828e-02]
 [2.1751988e-01 2.0340124e-01 2.1854685e-01 ... 6.2289760e-06
  1.2908275e-01 1.1261571e-02]
 [1.7128555e-01 1.6561384e-01 1.6562335e-01 ... 8.1730268e-06
  2.5651988e-01 6.5056823e-02]
 ...
 [1.9233678e-01 2.0489477e-01 2.0133489e-01 ... 2.2587730e-04
  1.5383600e-01 1.3153313e-02]
 [1.9378760e-01 2.2220470e-01 2.0713182e-01 ... 5.0976592e-05
  1.5088488e-01 4.7785244e-03]
 [6.3940927e-02 8.6044125e-02 7.0373505e-02 ... 6.3248477e-03
  7.0615910e-02 2.9341292e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 44.58 [%]
Global accuracy score (test) = 36.54 [%]
Global F1 score (train) = 43.58 [%]
Global F1 score (test) = 36.14 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.35      0.22      0.27       184
 CAMINAR CON MÓVIL O LIBRO       0.21      0.24      0.23       184
       CAMINAR USUAL SPEED       0.22      0.34      0.26       184
            CAMINAR ZIGZAG       0.18      0.12      0.14       184
          DE PIE BARRIENDO       0.24      0.54      0.33       184
   DE PIE DOBLANDO TOALLAS       0.35      0.10      0.15       184
    DE PIE MOVIENDO LIBROS       0.28      0.36      0.32       184
          DE PIE USANDO PC       0.43      0.54      0.48       184
        FASE REPOSO CON K5       0.60      0.78      0.68       184
INCREMENTAL CICLOERGOMETRO       0.94      0.60      0.74       184
           SENTADO LEYENDO       0.26      0.22      0.24       184
         SENTADO USANDO PC       0.31      0.11      0.17       184
      SENTADO VIENDO LA TV       0.34      0.42      0.38       184
   SUBIR Y BAJAR ESCALERAS       0.30      0.20      0.24       184
                    TROTAR       0.90      0.73      0.80       161

                  accuracy                           0.37      2737
                 macro avg       0.39      0.37      0.36      2737
              weighted avg       0.39      0.37      0.36      2737

2025-10-28 14:12:15.352913: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 14:12:15.364292: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1761657135.377394 2285471 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761657135.381611 2285471 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761657135.391316 2285471 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657135.391335 2285471 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657135.391337 2285471 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657135.391338 2285471 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 14:12:15.394573: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1761657137.754755 2285471 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13697 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/110
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761657139.501740 2285570 service.cc:152] XLA service 0x72486c01cff0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761657139.501802 2285570 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 14:12:19.537045: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
I0000 00:00:1761657139.700313 2285570 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761657141.933843 2285570 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16:40[0m 3s/step - accuracy: 0.1094 - loss: 3.5628
[1m 27/292[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0716 - loss: 3.6419  
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[1m 98/292[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0780 - loss: 3.5685
[1m138/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0803 - loss: 3.5404
[1m177/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0815 - loss: 3.5207
[1m216/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0823 - loss: 3.5055
[1m255/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0831 - loss: 3.4925
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step - accuracy: 0.0838 - loss: 3.4813
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 16ms/step - accuracy: 0.0838 - loss: 3.4810 - val_accuracy: 0.1521 - val_loss: 2.4974
Epoch 2/110

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Epoch 3/110

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Epoch 4/110

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Epoch 5/110

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[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1294 - loss: 2.8699
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1303 - loss: 2.8629 - val_accuracy: 0.2009 - val_loss: 2.3146
Epoch 6/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1402 - loss: 2.7516 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1425 - loss: 2.7534
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Epoch 7/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1619 - loss: 2.6503 
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[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1515 - loss: 2.6737
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Epoch 8/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1603 - loss: 2.6261 
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Epoch 9/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1250 - loss: 2.5857
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1647 - loss: 2.5414 
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1616 - loss: 2.5578
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1621 - loss: 2.5555
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1625 - loss: 2.5539 - val_accuracy: 0.2422 - val_loss: 2.1543
Epoch 10/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 32ms/step - accuracy: 0.1875 - loss: 2.4004
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1764 - loss: 2.5065 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1727 - loss: 2.5042
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[1m145/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1713 - loss: 2.5031
[1m181/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1715 - loss: 2.5017
[1m220/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1719 - loss: 2.4993
[1m259/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1724 - loss: 2.4974
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1729 - loss: 2.4956 - val_accuracy: 0.2403 - val_loss: 2.1100
Epoch 11/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1562 - loss: 2.4231
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1813 - loss: 2.4608 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1839 - loss: 2.4477
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1849 - loss: 2.4416
[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1851 - loss: 2.4386
[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1853 - loss: 2.4363
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1856 - loss: 2.4348
[1m256/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1856 - loss: 2.4336
[1m290/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1857 - loss: 2.4326
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1857 - loss: 2.4325 - val_accuracy: 0.2564 - val_loss: 2.0793
Epoch 12/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2031 - loss: 2.3312
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1790 - loss: 2.4119 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1799 - loss: 2.4121
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1815 - loss: 2.4099
[1m144/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1825 - loss: 2.4073
[1m179/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1832 - loss: 2.4048
[1m220/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1840 - loss: 2.4022
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1845 - loss: 2.4002
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Epoch 13/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.2500 - loss: 2.0360
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1967 - loss: 2.3248 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1942 - loss: 2.3435
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1941 - loss: 2.3471
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[1m183/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1939 - loss: 2.3483
[1m218/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1942 - loss: 2.3470
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Epoch 14/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2969 - loss: 2.3169
[1m 33/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1970 - loss: 2.3372 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1981 - loss: 2.3350
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[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2007 - loss: 2.3271
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2004 - loss: 2.3263
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2005 - loss: 2.3229 - val_accuracy: 0.2659 - val_loss: 2.0081
Epoch 15/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1719 - loss: 2.1168
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1992 - loss: 2.2557 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2041 - loss: 2.2646
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[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2054 - loss: 2.2746
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2058 - loss: 2.2749
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2066 - loss: 2.2743
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2071 - loss: 2.2739
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2074 - loss: 2.2736 - val_accuracy: 0.2747 - val_loss: 1.9788
Epoch 16/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2500 - loss: 2.0836
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2079 - loss: 2.2335 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2062 - loss: 2.2439
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2072 - loss: 2.2437
[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2087 - loss: 2.2422
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2097 - loss: 2.2418
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2105 - loss: 2.2416
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2111 - loss: 2.2415
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2115 - loss: 2.2411 - val_accuracy: 0.2673 - val_loss: 1.9665
Epoch 17/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2656 - loss: 2.1756
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2154 - loss: 2.2036 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2119 - loss: 2.2146
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2113 - loss: 2.2184
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2115 - loss: 2.2210
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2122 - loss: 2.2218
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2132 - loss: 2.2208
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2139 - loss: 2.2202
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2142 - loss: 2.2197 - val_accuracy: 0.2849 - val_loss: 1.9431
Epoch 18/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1562 - loss: 2.1512
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2186 - loss: 2.1908 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2249 - loss: 2.1853
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[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2273 - loss: 2.1807
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2277 - loss: 2.1813
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2278 - loss: 2.1822
[1m263/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2280 - loss: 2.1829
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2279 - loss: 2.1836 - val_accuracy: 0.2853 - val_loss: 1.9163
Epoch 19/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2969 - loss: 2.1283
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2303 - loss: 2.1963 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2288 - loss: 2.1956
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[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2311 - loss: 2.1881
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2314 - loss: 2.1820
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2313 - loss: 2.1802
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2311 - loss: 2.1796 - val_accuracy: 0.2899 - val_loss: 1.9126
Epoch 20/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2031 - loss: 2.0650
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2118 - loss: 2.1707 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2190 - loss: 2.1666
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[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2240 - loss: 2.1624
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2248 - loss: 2.1613
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2256 - loss: 2.1601
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2262 - loss: 2.1591
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2266 - loss: 2.1584 - val_accuracy: 0.2955 - val_loss: 1.9127
Epoch 21/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2656 - loss: 2.1175
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2369 - loss: 2.1663 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2376 - loss: 2.1535
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2380 - loss: 2.1486
[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2388 - loss: 2.1445
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2388 - loss: 2.1431
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2387 - loss: 2.1421
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2388 - loss: 2.1410
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2388 - loss: 2.1406 - val_accuracy: 0.3108 - val_loss: 1.8749
Epoch 22/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1875 - loss: 2.3559
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2424 - loss: 2.1387 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2430 - loss: 2.1258
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2418 - loss: 2.1212
[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2410 - loss: 2.1189
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2408 - loss: 2.1173
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2406 - loss: 2.1163
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2405 - loss: 2.1159
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2405 - loss: 2.1154 - val_accuracy: 0.3166 - val_loss: 1.8511
Epoch 23/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2344 - loss: 2.1107
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2328 - loss: 2.1306 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2380 - loss: 2.1248
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2379 - loss: 2.1228
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2382 - loss: 2.1199
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2390 - loss: 2.1172
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2398 - loss: 2.1149
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2406 - loss: 2.1127
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2411 - loss: 2.1115 - val_accuracy: 0.3045 - val_loss: 1.8525
Epoch 24/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.2656 - loss: 2.1810
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2494 - loss: 2.1159 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2503 - loss: 2.1040
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[1m218/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2475 - loss: 2.0985
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2477 - loss: 2.0955 - val_accuracy: 0.3110 - val_loss: 1.8459
Epoch 25/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1875 - loss: 2.1666
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2384 - loss: 2.0664 
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2418 - loss: 2.0654
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2425 - loss: 2.0654
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2430 - loss: 2.0652
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2434 - loss: 2.0651
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2436 - loss: 2.0651 - val_accuracy: 0.2934 - val_loss: 1.8373
Epoch 26/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3281 - loss: 2.0369
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2520 - loss: 2.0683 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2526 - loss: 2.0656
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[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2503 - loss: 2.0597
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2499 - loss: 2.0581
[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2497 - loss: 2.0576
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2498 - loss: 2.0579
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2498 - loss: 2.0583 - val_accuracy: 0.3166 - val_loss: 1.8219
Epoch 27/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3281 - loss: 1.9382
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2704 - loss: 2.0468 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2673 - loss: 2.0466
[1m109/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2634 - loss: 2.0482
[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2613 - loss: 2.0476
[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2606 - loss: 2.0463
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2602 - loss: 2.0449
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2601 - loss: 2.0438
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2601 - loss: 2.0428 - val_accuracy: 0.3201 - val_loss: 1.8060
Epoch 28/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1875 - loss: 2.1410
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2426 - loss: 2.0813 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2500 - loss: 2.0687
[1m111/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2528 - loss: 2.0616
[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2538 - loss: 2.0567
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2539 - loss: 2.0545
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2543 - loss: 2.0531
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2548 - loss: 2.0519
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2550 - loss: 2.0513 - val_accuracy: 0.3227 - val_loss: 1.8009
Epoch 29/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2656 - loss: 1.8848
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2651 - loss: 2.0105 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2659 - loss: 2.0111
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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2653 - loss: 2.0177
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2651 - loss: 2.0184
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Epoch 30/110

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[1m 34/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2869 - loss: 2.0084 
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Epoch 31/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2568 - loss: 2.0251 
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[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2663 - loss: 2.0141
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2674 - loss: 2.0128 - val_accuracy: 0.3241 - val_loss: 1.7789
Epoch 32/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1719 - loss: 2.1444
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2663 - loss: 2.0471 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2697 - loss: 2.0362
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[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2716 - loss: 2.0241
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2724 - loss: 2.0192
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2730 - loss: 2.0162
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2736 - loss: 2.0137
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2740 - loss: 2.0119 - val_accuracy: 0.3297 - val_loss: 1.7649
Epoch 33/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.3750 - loss: 1.9203
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2683 - loss: 2.0176 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2687 - loss: 2.0062
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[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2718 - loss: 1.9961
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2722 - loss: 1.9950
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2726 - loss: 1.9934
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2732 - loss: 1.9921
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2737 - loss: 1.9909 - val_accuracy: 0.3321 - val_loss: 1.7593
Epoch 34/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2969 - loss: 1.8717
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2961 - loss: 1.9820 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2940 - loss: 1.9728
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[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2896 - loss: 1.9716
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2882 - loss: 1.9730
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2872 - loss: 1.9736
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2862 - loss: 1.9740
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Epoch 35/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2718 - loss: 1.9539 
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Epoch 36/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2824 - loss: 1.9835 
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Epoch 37/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3060 - loss: 1.9120 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2994 - loss: 1.9190
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2889 - loss: 1.9325 - val_accuracy: 0.3439 - val_loss: 1.7396
Epoch 38/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2646 - loss: 1.9500 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2779 - loss: 1.9492
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[1m180/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2864 - loss: 1.9468
[1m219/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2874 - loss: 1.9459
[1m257/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2880 - loss: 1.9450
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2882 - loss: 1.9444 - val_accuracy: 0.3295 - val_loss: 1.7257
Epoch 39/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3144 - loss: 1.9177 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3133 - loss: 1.9192
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3057 - loss: 1.9289
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3044 - loss: 1.9300
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Epoch 40/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2838 - loss: 1.9272 
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Epoch 41/110

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Epoch 42/110

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Epoch 43/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3072 - loss: 1.9017 
[1m 82/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3036 - loss: 1.9041
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[1m239/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3063 - loss: 1.9017
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3064 - loss: 1.9015
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3063 - loss: 1.9013 - val_accuracy: 0.3469 - val_loss: 1.6958
Epoch 44/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3092 - loss: 1.8740 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3055 - loss: 1.8878
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[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3054 - loss: 1.8912
[1m222/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3051 - loss: 1.8917
[1m259/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3049 - loss: 1.8923
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3049 - loss: 1.8929 - val_accuracy: 0.3271 - val_loss: 1.6945
Epoch 45/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3063 - loss: 1.9137 
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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3040 - loss: 1.9065
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Epoch 46/110

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Epoch 47/110

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Epoch 48/110

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[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3118 - loss: 1.8641
[1m291/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3121 - loss: 1.8654
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3121 - loss: 1.8655 - val_accuracy: 0.3519 - val_loss: 1.6644
Epoch 49/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3064 - loss: 1.8802 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3049 - loss: 1.8772
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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3045 - loss: 1.8769
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3052 - loss: 1.8768
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3058 - loss: 1.8771
[1m263/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3065 - loss: 1.8770
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3069 - loss: 1.8771 - val_accuracy: 0.3562 - val_loss: 1.6579
Epoch 50/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3237 - loss: 1.8761 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3215 - loss: 1.8747
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[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3184 - loss: 1.8719
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3176 - loss: 1.8713
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3173 - loss: 1.8700
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Epoch 51/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3247 - loss: 1.8582 
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Epoch 52/110

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Epoch 53/110

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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3191 - loss: 1.8367 - val_accuracy: 0.3739 - val_loss: 1.6432
Epoch 54/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3134 - loss: 1.8693 
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3231 - loss: 1.8404
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3229 - loss: 1.8406
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3229 - loss: 1.8405 - val_accuracy: 0.3617 - val_loss: 1.6426
Epoch 55/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3594 - loss: 1.8839
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3507 - loss: 1.8404 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3431 - loss: 1.8318
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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3352 - loss: 1.8297
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3343 - loss: 1.8302
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Epoch 56/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2500 - loss: 2.0488
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3081 - loss: 1.8988 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3147 - loss: 1.8802
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Epoch 57/110

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Epoch 58/110

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Epoch 59/110

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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3229 - loss: 1.8266
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3236 - loss: 1.8269 - val_accuracy: 0.3611 - val_loss: 1.6247
Epoch 60/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3554 - loss: 1.7748 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3497 - loss: 1.7949
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3409 - loss: 1.8099
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3395 - loss: 1.8117 - val_accuracy: 0.3584 - val_loss: 1.6309
Epoch 61/110

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[1m 33/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3539 - loss: 1.7835 
[1m 68/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3424 - loss: 1.8028
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[1m286/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3341 - loss: 1.8148
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Epoch 62/110

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Epoch 63/110

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Epoch 64/110

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Epoch 65/110

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Epoch 66/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3378 - loss: 1.7824 
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Epoch 67/110

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[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3398 - loss: 1.7896
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3399 - loss: 1.7888
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3399 - loss: 1.7884
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3398 - loss: 1.7884 - val_accuracy: 0.3758 - val_loss: 1.6194
Epoch 68/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3125 - loss: 1.8063
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3389 - loss: 1.8050 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3391 - loss: 1.8014
[1m109/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3389 - loss: 1.7995
[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3387 - loss: 1.7973
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3386 - loss: 1.7956
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3384 - loss: 1.7947
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3382 - loss: 1.7942
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3382 - loss: 1.7937 - val_accuracy: 0.3745 - val_loss: 1.6226
Epoch 69/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3281 - loss: 1.7318
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3458 - loss: 1.7340 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3455 - loss: 1.7436
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3436 - loss: 1.7538
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3425 - loss: 1.7597
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3415 - loss: 1.7650
[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3410 - loss: 1.7686
[1m257/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3407 - loss: 1.7709
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3406 - loss: 1.7728 - val_accuracy: 0.3611 - val_loss: 1.6311

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:13[0m 863ms/step
[1m62/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 823us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 9ms/step
Saved model to disk.

Accuracy capturado en la ejecución 10: 36.54 [%]
F1-score capturado en la ejecución 10: 36.14 [%]

=== EJECUCIÓN 11 ===

--- TRAIN (ejecución 11) ---

--- TEST (ejecución 11) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18676, 6, 250)

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:46[0m 903ms/step
[1m 61/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 844us/step  
[1m136/584[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 750us/step
[1m210/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 727us/step
[1m278/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 731us/step
[1m344/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 736us/step
[1m415/584[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 732us/step
[1m479/584[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 739us/step
[1m549/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 736us/step
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m58/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 878us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 65/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 784us/step
[1m140/169[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 726us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 36.11 [%]
Global F1 score (validation) = 34.65 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[2.0720139e-01 2.0540227e-01 2.0490259e-01 ... 6.7390242e-06
  1.7073384e-01 1.6528863e-02]
 [2.1796240e-01 2.0000897e-01 1.9503081e-01 ... 8.6884802e-06
  1.4671850e-01 1.0300057e-02]
 [2.0996931e-01 2.0282993e-01 1.9884035e-01 ... 1.3214149e-05
  1.6112733e-01 1.0553047e-02]
 ...
 [1.9915809e-01 1.9356456e-01 1.8606335e-01 ... 1.7206819e-04
  1.7096174e-01 2.2051992e-02]
 [1.8424577e-01 1.8576030e-01 1.7865480e-01 ... 2.8118608e-04
  1.9429541e-01 3.3420101e-02]
 [4.1024860e-02 5.3030699e-02 4.0368743e-02 ... 2.9426476e-03
  4.7164205e-02 1.8419350e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 41.65 [%]
Global accuracy score (test) = 36.06 [%]
Global F1 score (train) = 39.61 [%]
Global F1 score (test) = 34.35 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.25      0.56      0.35       184
 CAMINAR CON MÓVIL O LIBRO       0.22      0.30      0.25       184
       CAMINAR USUAL SPEED       0.00      0.00      0.00       184
            CAMINAR ZIGZAG       0.14      0.13      0.14       184
          DE PIE BARRIENDO       0.22      0.45      0.29       184
   DE PIE DOBLANDO TOALLAS       0.33      0.13      0.19       184
    DE PIE MOVIENDO LIBROS       0.28      0.34      0.30       184
          DE PIE USANDO PC       0.37      0.57      0.45       184
        FASE REPOSO CON K5       0.55      0.85      0.67       184
INCREMENTAL CICLOERGOMETRO       0.94      0.60      0.74       184
           SENTADO LEYENDO       0.34      0.28      0.31       184
         SENTADO USANDO PC       0.22      0.11      0.15       184
      SENTADO VIENDO LA TV       0.43      0.34      0.38       184
   SUBIR Y BAJAR ESCALERAS       0.61      0.08      0.14       184
                    TROTAR       0.93      0.71      0.81       161

                  accuracy                           0.36      2737
                 macro avg       0.39      0.36      0.34      2737
              weighted avg       0.38      0.36      0.34      2737

2025-10-28 14:13:17.102931: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 14:13:17.114890: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1761657197.128940 2292913 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761657197.133376 2292913 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761657197.143666 2292913 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657197.143689 2292913 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657197.143691 2292913 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657197.143693 2292913 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 14:13:17.147017: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1761657199.526343 2292913 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13697 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/110
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761657201.232769 2293022 service.cc:152] XLA service 0x7a39c4015650 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761657201.232834 2293022 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 14:13:21.270790: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
I0000 00:00:1761657201.441400 2293022 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761657203.744484 2293022 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/110

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Epoch 3/110

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Epoch 4/110

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Epoch 5/110

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Epoch 6/110

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Epoch 7/110

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[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1538 - loss: 2.6603
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1535 - loss: 2.6605 - val_accuracy: 0.2148 - val_loss: 2.2330
Epoch 8/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1606 - loss: 2.6133 
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1608 - loss: 2.6137
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1612 - loss: 2.6106
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Epoch 9/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1734 - loss: 2.5781 
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Epoch 10/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1889 - loss: 2.4784 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1843 - loss: 2.4908
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[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1787 - loss: 2.4903
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1773 - loss: 2.4868 - val_accuracy: 0.2540 - val_loss: 2.0858
Epoch 11/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1804 - loss: 2.4432 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1767 - loss: 2.4372
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1761 - loss: 2.4384
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1765 - loss: 2.4376
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1769 - loss: 2.4367 - val_accuracy: 0.2457 - val_loss: 2.0721
Epoch 12/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1938 - loss: 2.4059 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1913 - loss: 2.4059
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1923 - loss: 2.4010
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[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1920 - loss: 2.3960
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1916 - loss: 2.3946
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1912 - loss: 2.3939
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1910 - loss: 2.3934 - val_accuracy: 0.2570 - val_loss: 2.0340
Epoch 13/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1875 - loss: 2.3630
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1889 - loss: 2.3614 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1910 - loss: 2.3535
[1m109/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1916 - loss: 2.3517
[1m147/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1922 - loss: 2.3508
[1m183/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1923 - loss: 2.3500
[1m222/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1920 - loss: 2.3495
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1919 - loss: 2.3493
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1918 - loss: 2.3489 - val_accuracy: 0.2655 - val_loss: 2.0042
Epoch 14/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2059 - loss: 2.3049 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2042 - loss: 2.2979
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2027 - loss: 2.3002
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2013 - loss: 2.3015
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2004 - loss: 2.3020
[1m220/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1997 - loss: 2.3025
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1991 - loss: 2.3029
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1987 - loss: 2.3030 - val_accuracy: 0.2673 - val_loss: 1.9906
Epoch 15/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1904 - loss: 2.2622 
[1m 70/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1894 - loss: 2.2739
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[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1976 - loss: 2.2761
[1m259/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1988 - loss: 2.2756
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1995 - loss: 2.2751 - val_accuracy: 0.2679 - val_loss: 1.9555
Epoch 16/110

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Epoch 17/110

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[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2199 - loss: 2.2108
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[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2162 - loss: 2.2156
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2160 - loss: 2.2150 - val_accuracy: 0.2790 - val_loss: 1.9236
Epoch 18/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2656 - loss: 2.2023
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2237 - loss: 2.1884 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2208 - loss: 2.1903
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[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2187 - loss: 2.1885
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2185 - loss: 2.1880
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2184 - loss: 2.1883
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2183 - loss: 2.1882
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2182 - loss: 2.1881 - val_accuracy: 0.2809 - val_loss: 1.9075
Epoch 19/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2031 - loss: 2.0680
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2215 - loss: 2.1673 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2241 - loss: 2.1730
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2244 - loss: 2.1746
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2245 - loss: 2.1759
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2246 - loss: 2.1755
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2248 - loss: 2.1751
[1m262/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2249 - loss: 2.1745
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2249 - loss: 2.1740 - val_accuracy: 0.2749 - val_loss: 1.9035
Epoch 20/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3594 - loss: 1.9388
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2329 - loss: 2.1493 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2295 - loss: 2.1611
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2286 - loss: 2.1634
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2272 - loss: 2.1636
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2268 - loss: 2.1631
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2267 - loss: 2.1624
[1m263/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2267 - loss: 2.1612
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2269 - loss: 2.1597 - val_accuracy: 0.2873 - val_loss: 1.8847
Epoch 21/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2318 - loss: 2.1525 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2341 - loss: 2.1472
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2349 - loss: 2.1370 - val_accuracy: 0.2855 - val_loss: 1.8694
Epoch 22/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2188 - loss: 2.2426
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2394 - loss: 2.1151 
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[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2382 - loss: 2.1100
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2381 - loss: 2.1107 - val_accuracy: 0.2786 - val_loss: 1.8719
Epoch 23/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2431 - loss: 2.0824 
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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2456 - loss: 2.0919
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2448 - loss: 2.0933
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2441 - loss: 2.0937
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2436 - loss: 2.0938
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2432 - loss: 2.0938 - val_accuracy: 0.2912 - val_loss: 1.8450
Epoch 24/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2500 - loss: 2.0378
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2406 - loss: 2.0905 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2432 - loss: 2.0898
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2422 - loss: 2.0916
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2418 - loss: 2.0922
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2419 - loss: 2.0917
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2424 - loss: 2.0904
[1m263/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2430 - loss: 2.0893
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2433 - loss: 2.0883 - val_accuracy: 0.2912 - val_loss: 1.8390
Epoch 25/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2031 - loss: 2.0843
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2373 - loss: 2.0736 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2427 - loss: 2.0686
[1m119/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2444 - loss: 2.0687
[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2458 - loss: 2.0689
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2467 - loss: 2.0696
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2472 - loss: 2.0708
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2478 - loss: 2.0710
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2480 - loss: 2.0710 - val_accuracy: 0.2925 - val_loss: 1.8398
Epoch 26/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2454 - loss: 2.0720 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2458 - loss: 2.0669
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2486 - loss: 2.0623
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2486 - loss: 2.0627
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2487 - loss: 2.0626 - val_accuracy: 0.3003 - val_loss: 1.8282
Epoch 27/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2344 - loss: 2.1232
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2547 - loss: 2.0511 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2517 - loss: 2.0442
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2500 - loss: 2.0455
[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2494 - loss: 2.0459
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2494 - loss: 2.0457
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2497 - loss: 2.0455
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2499 - loss: 2.0452
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2501 - loss: 2.0451 - val_accuracy: 0.3084 - val_loss: 1.8248
Epoch 28/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2812 - loss: 2.0627
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2445 - loss: 2.0569 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2459 - loss: 2.0503
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[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2488 - loss: 2.0422
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2501 - loss: 2.0402
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2512 - loss: 2.0391
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2521 - loss: 2.0377
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2526 - loss: 2.0370 - val_accuracy: 0.3092 - val_loss: 1.8245
Epoch 29/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2969 - loss: 1.9859
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2860 - loss: 1.9739 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2839 - loss: 1.9861
[1m111/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2816 - loss: 1.9931
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2789 - loss: 1.9995
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2767 - loss: 2.0036
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2748 - loss: 2.0063
[1m262/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2734 - loss: 2.0084
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2724 - loss: 2.0095 - val_accuracy: 0.3014 - val_loss: 1.8119
Epoch 30/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2500 - loss: 1.9881
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2638 - loss: 2.0190 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2640 - loss: 2.0188
[1m108/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2653 - loss: 2.0171
[1m146/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2660 - loss: 2.0149
[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2656 - loss: 2.0153
[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2655 - loss: 2.0161
[1m262/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2655 - loss: 2.0160
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2656 - loss: 2.0157 - val_accuracy: 0.3108 - val_loss: 1.7957
Epoch 31/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3750 - loss: 1.8401
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2766 - loss: 1.9961 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2707 - loss: 1.9979
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2700 - loss: 1.9978
[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2693 - loss: 1.9982
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2689 - loss: 1.9982
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2686 - loss: 1.9983
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2685 - loss: 1.9983
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2685 - loss: 1.9984 - val_accuracy: 0.3214 - val_loss: 1.7915
Epoch 32/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2664 - loss: 1.9623 
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Epoch 33/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2822 - loss: 2.0012 
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Epoch 34/110

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[1m 31/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2837 - loss: 1.9805 
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[1m217/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2739 - loss: 1.9907
[1m253/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2739 - loss: 1.9894
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Epoch 35/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2737 - loss: 1.9727 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2748 - loss: 1.9673
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[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2778 - loss: 1.9668
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2780 - loss: 1.9675
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2786 - loss: 1.9683 - val_accuracy: 0.3284 - val_loss: 1.7661
Epoch 36/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2928 - loss: 1.9864 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2884 - loss: 1.9811
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[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2832 - loss: 1.9748
[1m199/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2823 - loss: 1.9713
[1m237/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2816 - loss: 1.9699
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Epoch 37/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2990 - loss: 1.9563 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2975 - loss: 1.9521
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[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2936 - loss: 1.9482
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2923 - loss: 1.9490 - val_accuracy: 0.3332 - val_loss: 1.7508
Epoch 38/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3281 - loss: 1.8155
[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2878 - loss: 1.9474 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2920 - loss: 1.9432
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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2895 - loss: 1.9440
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Epoch 39/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2943 - loss: 1.9566 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2977 - loss: 1.9480
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[1m237/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2940 - loss: 1.9444
[1m276/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2935 - loss: 1.9438
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2934 - loss: 1.9434 - val_accuracy: 0.3312 - val_loss: 1.7410
Epoch 40/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 24ms/step - accuracy: 0.3281 - loss: 1.9910
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2935 - loss: 1.9640 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2938 - loss: 1.9420
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[1m182/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2930 - loss: 1.9321
[1m220/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2929 - loss: 1.9321
[1m256/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2930 - loss: 1.9322
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2931 - loss: 1.9323 - val_accuracy: 0.3471 - val_loss: 1.7194
Epoch 41/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3125 - loss: 1.9273
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2955 - loss: 1.9347 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2944 - loss: 1.9287
[1m109/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2957 - loss: 1.9256
[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2958 - loss: 1.9241
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2956 - loss: 1.9241
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2958 - loss: 1.9239
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2959 - loss: 1.9231
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2960 - loss: 1.9230 - val_accuracy: 0.3438 - val_loss: 1.7234
Epoch 42/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3438 - loss: 1.8123
[1m 33/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3067 - loss: 1.9022 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2992 - loss: 1.9139
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[1m147/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2972 - loss: 1.9222
[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2968 - loss: 1.9225
[1m217/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2967 - loss: 1.9223
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Epoch 43/110

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Epoch 44/110

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Epoch 45/110

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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3016 - loss: 1.9196
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3021 - loss: 1.9175
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3022 - loss: 1.9165 - val_accuracy: 0.3402 - val_loss: 1.7020
Epoch 46/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3077 - loss: 1.9165 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3074 - loss: 1.9038
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[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3060 - loss: 1.8990
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3060 - loss: 1.8976
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3059 - loss: 1.8964
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Epoch 47/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3411 - loss: 1.8393 
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[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3134 - loss: 1.8771
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Epoch 48/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3092 - loss: 1.9040 
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Epoch 49/110

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Epoch 50/110

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Epoch 51/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3195 - loss: 1.8784 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3160 - loss: 1.8741
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[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3125 - loss: 1.8746
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3124 - loss: 1.8749
[1m259/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3125 - loss: 1.8751
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3126 - loss: 1.8751 - val_accuracy: 0.3519 - val_loss: 1.6694
Epoch 52/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3163 - loss: 1.8247 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3120 - loss: 1.8419
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[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3146 - loss: 1.8526
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3152 - loss: 1.8537
[1m263/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3160 - loss: 1.8540
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3165 - loss: 1.8539 - val_accuracy: 0.3478 - val_loss: 1.6659
Epoch 53/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3183 - loss: 1.8185 
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3158 - loss: 1.8370
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Epoch 54/110

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Epoch 55/110

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Epoch 56/110

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Epoch 57/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3333 - loss: 1.8123 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3296 - loss: 1.8328
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3240 - loss: 1.8396
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Epoch 58/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3270 - loss: 1.7936 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3271 - loss: 1.8017
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Epoch 59/110

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[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3147 - loss: 1.8807
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Epoch 60/110

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Epoch 61/110

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Epoch 62/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3391 - loss: 1.8112 
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[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3259 - loss: 1.8263
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3255 - loss: 1.8260 - val_accuracy: 0.3713 - val_loss: 1.6325
Epoch 63/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3477 - loss: 1.7695 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3484 - loss: 1.7687
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[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3441 - loss: 1.7817
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3420 - loss: 1.7860
[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3404 - loss: 1.7889
[1m276/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3392 - loss: 1.7911
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3388 - loss: 1.7920 - val_accuracy: 0.3587 - val_loss: 1.6492
Epoch 64/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3363 - loss: 1.8222 
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[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3284 - loss: 1.8311
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3285 - loss: 1.8301
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Epoch 65/110

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Epoch 66/110

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Epoch 67/110

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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3333 - loss: 1.7989 - val_accuracy: 0.3558 - val_loss: 1.6262
Epoch 68/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3180 - loss: 1.7737 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3252 - loss: 1.7764
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[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3309 - loss: 1.7919
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Epoch 69/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3251 - loss: 1.8149 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3284 - loss: 1.8050
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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3344 - loss: 1.7887
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Epoch 70/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3623 - loss: 1.7604 
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Epoch 71/110

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Epoch 72/110

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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3382 - loss: 1.7919
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3385 - loss: 1.7903 - val_accuracy: 0.3500 - val_loss: 1.6233
Epoch 73/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3438 - loss: 1.7714
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3263 - loss: 1.7672 
[1m 70/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3299 - loss: 1.7650
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[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3354 - loss: 1.7736
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3357 - loss: 1.7742
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3358 - loss: 1.7745 - val_accuracy: 0.3508 - val_loss: 1.6239
Epoch 74/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3536 - loss: 1.7662 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3480 - loss: 1.7673
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3461 - loss: 1.7664
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3458 - loss: 1.7663
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3459 - loss: 1.7665
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3458 - loss: 1.7671
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3458 - loss: 1.7675
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3457 - loss: 1.7681 - val_accuracy: 0.3702 - val_loss: 1.6156
Epoch 75/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3624 - loss: 1.7531 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3537 - loss: 1.7674
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[1m147/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3479 - loss: 1.7714
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3467 - loss: 1.7724
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3459 - loss: 1.7737
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3452 - loss: 1.7746
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Epoch 76/110

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[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3661 - loss: 1.7269
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Epoch 77/110

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Epoch 78/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3807 - loss: 1.7460 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3659 - loss: 1.7519
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[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3547 - loss: 1.7586
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3522 - loss: 1.7597
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3506 - loss: 1.7602
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3488 - loss: 1.7604 - val_accuracy: 0.3647 - val_loss: 1.6279
Epoch 79/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3281 - loss: 1.8392
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3650 - loss: 1.7540 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3589 - loss: 1.7420
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3555 - loss: 1.7433
[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3529 - loss: 1.7460
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3520 - loss: 1.7485
[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3516 - loss: 1.7502
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3510 - loss: 1.7518
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3508 - loss: 1.7526 - val_accuracy: 0.3697 - val_loss: 1.6153
Epoch 80/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3698 - loss: 1.7003 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3617 - loss: 1.7259
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3565 - loss: 1.7379
[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3532 - loss: 1.7441
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3520 - loss: 1.7466
[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3513 - loss: 1.7485
[1m276/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3505 - loss: 1.7503
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Epoch 81/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3341 - loss: 1.7762 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3373 - loss: 1.7761
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Epoch 82/110

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Epoch 83/110

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Epoch 84/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3534 - loss: 1.7418 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3514 - loss: 1.7447
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3469 - loss: 1.7481
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3464 - loss: 1.7489 - val_accuracy: 0.3643 - val_loss: 1.6140
Epoch 85/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3451 - loss: 1.7527 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3422 - loss: 1.7490
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[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3406 - loss: 1.7492
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3407 - loss: 1.7497
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3409 - loss: 1.7503
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3410 - loss: 1.7507 - val_accuracy: 0.3682 - val_loss: 1.6201
Epoch 86/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3690 - loss: 1.7064 
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[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3575 - loss: 1.7369
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3567 - loss: 1.7379
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Epoch 87/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3563 - loss: 1.7785 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3510 - loss: 1.7685
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Epoch 88/110

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[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3393 - loss: 1.7485
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[1m253/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3448 - loss: 1.7513
[1m290/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3452 - loss: 1.7511
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3452 - loss: 1.7511 - val_accuracy: 0.3597 - val_loss: 1.6104
Epoch 89/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3734 - loss: 1.6691 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3724 - loss: 1.6844
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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3644 - loss: 1.7091
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3619 - loss: 1.7154
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3600 - loss: 1.7194
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3591 - loss: 1.7220
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3585 - loss: 1.7232 - val_accuracy: 0.3676 - val_loss: 1.6153
Epoch 90/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3624 - loss: 1.7176 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3564 - loss: 1.7254
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3536 - loss: 1.7267
[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3527 - loss: 1.7281
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3523 - loss: 1.7293
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3520 - loss: 1.7298
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3516 - loss: 1.7308
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3514 - loss: 1.7312 - val_accuracy: 0.3815 - val_loss: 1.6278
Epoch 91/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3750 - loss: 1.7077
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3571 - loss: 1.7693 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3520 - loss: 1.7622
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3502 - loss: 1.7578
[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3491 - loss: 1.7554
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3486 - loss: 1.7530
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3487 - loss: 1.7518
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3489 - loss: 1.7506
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Epoch 92/110

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[1m 32/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3626 - loss: 1.7267 
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Epoch 93/110

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[1m66/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 771us/step  
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Saved model to disk.

Accuracy capturado en la ejecución 11: 36.06 [%]
F1-score capturado en la ejecución 11: 34.35 [%]

=== EJECUCIÓN 12 ===

--- TRAIN (ejecución 12) ---

--- TEST (ejecución 12) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18676, 6, 250)

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[1m 66/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 771us/step
[1m128/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 791us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 37.19 [%]
Global F1 score (validation) = 37.67 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[2.0965511e-01 1.8895638e-01 2.0952938e-01 ... 2.1904159e-06
  1.5944336e-01 2.2886077e-02]
 [2.1670051e-01 2.0801860e-01 2.0207880e-01 ... 4.3293230e-06
  1.2557673e-01 7.4639288e-03]
 [1.9634253e-01 1.7102173e-01 2.0288736e-01 ... 4.9054274e-06
  1.7505269e-01 5.6401353e-02]
 ...
 [1.9170630e-01 2.0084421e-01 1.9705743e-01 ... 4.4344179e-05
  1.7990197e-01 2.4087045e-02]
 [1.9323391e-01 2.1890263e-01 1.8995321e-01 ... 4.4367895e-05
  1.6446277e-01 6.5624239e-03]
 [9.4960906e-02 1.2086871e-01 9.7668432e-02 ... 1.4199086e-03
  1.1664756e-01 5.4458622e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 44.03 [%]
Global accuracy score (test) = 37.82 [%]
Global F1 score (train) = 43.47 [%]
Global F1 score (test) = 37.35 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.30      0.34      0.32       184
 CAMINAR CON MÓVIL O LIBRO       0.21      0.34      0.26       184
       CAMINAR USUAL SPEED       0.30      0.21      0.25       184
            CAMINAR ZIGZAG       0.21      0.18      0.19       184
          DE PIE BARRIENDO       0.24      0.50      0.33       184
   DE PIE DOBLANDO TOALLAS       0.24      0.07      0.11       184
    DE PIE MOVIENDO LIBROS       0.26      0.32      0.29       184
          DE PIE USANDO PC       0.44      0.61      0.51       184
        FASE REPOSO CON K5       0.67      0.79      0.72       184
INCREMENTAL CICLOERGOMETRO       0.86      0.62      0.72       184
           SENTADO LEYENDO       0.33      0.38      0.35       184
         SENTADO USANDO PC       0.31      0.11      0.16       184
      SENTADO VIENDO LA TV       0.37      0.38      0.37       184
   SUBIR Y BAJAR ESCALERAS       0.39      0.20      0.27       184
                    TROTAR       0.87      0.68      0.76       161

                  accuracy                           0.38      2737
                 macro avg       0.40      0.38      0.37      2737
              weighted avg       0.40      0.38      0.37      2737

2025-10-28 14:14:32.223368: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 14:14:32.234858: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1761657272.248084 2302631 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761657272.252307 2302631 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761657272.262212 2302631 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657272.262235 2302631 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657272.262237 2302631 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657272.262239 2302631 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 14:14:32.265535: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1761657274.662907 2302631 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13697 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/110
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761657276.417754 2302741 service.cc:152] XLA service 0x76bb8800d5d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761657276.417790 2302741 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 14:14:36.451077: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
I0000 00:00:1761657276.621650 2302741 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761657278.906444 2302741 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/110

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Epoch 3/110

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Epoch 4/110

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Epoch 5/110

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Epoch 6/110

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Epoch 7/110

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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1438 - loss: 2.7137
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1432 - loss: 2.7112 - val_accuracy: 0.1763 - val_loss: 2.3048
Epoch 8/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1374 - loss: 2.6893 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1398 - loss: 2.6789
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1413 - loss: 2.6755
[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1425 - loss: 2.6733
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1434 - loss: 2.6717
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1443 - loss: 2.6682
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1449 - loss: 2.6652
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1453 - loss: 2.6630 - val_accuracy: 0.1948 - val_loss: 2.2679
Epoch 9/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1562 - loss: 2.6986
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1533 - loss: 2.6080 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1528 - loss: 2.6094
[1m120/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1529 - loss: 2.6091
[1m160/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1534 - loss: 2.6069
[1m200/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1539 - loss: 2.6041
[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1545 - loss: 2.6016
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1551 - loss: 2.5994
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1554 - loss: 2.5981 - val_accuracy: 0.2105 - val_loss: 2.2241
Epoch 10/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1596 - loss: 2.5815 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1597 - loss: 2.5729
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[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1603 - loss: 2.5569
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1608 - loss: 2.5535
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1618 - loss: 2.5477 - val_accuracy: 0.2246 - val_loss: 2.1809
Epoch 11/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1250 - loss: 2.5995
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1583 - loss: 2.5220 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1638 - loss: 2.5053
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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1716 - loss: 2.4871
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1719 - loss: 2.4854
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1721 - loss: 2.4843 - val_accuracy: 0.2398 - val_loss: 2.1438
Epoch 12/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2031 - loss: 2.4412
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1836 - loss: 2.4206 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1837 - loss: 2.4199
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[1m162/292[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1817 - loss: 2.4213
[1m201/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1813 - loss: 2.4208
[1m242/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1813 - loss: 2.4198
[1m282/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1814 - loss: 2.4190
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1814 - loss: 2.4188 - val_accuracy: 0.2442 - val_loss: 2.0981
Epoch 13/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2031 - loss: 2.2793
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1794 - loss: 2.3917 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1844 - loss: 2.3916
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1857 - loss: 2.3925
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1860 - loss: 2.3917
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1862 - loss: 2.3904
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1865 - loss: 2.3888
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1866 - loss: 2.3870
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1867 - loss: 2.3859 - val_accuracy: 0.2561 - val_loss: 2.0605
Epoch 14/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1719 - loss: 2.3289
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2099 - loss: 2.3165 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2056 - loss: 2.3161
[1m119/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2027 - loss: 2.3215
[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2006 - loss: 2.3264
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1992 - loss: 2.3296
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1979 - loss: 2.3319
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1970 - loss: 2.3327
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1967 - loss: 2.3328 - val_accuracy: 0.2655 - val_loss: 2.0276
Epoch 15/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1094 - loss: 2.3605
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1964 - loss: 2.2985 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2004 - loss: 2.3014
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1995 - loss: 2.3038
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1987 - loss: 2.3049
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1976 - loss: 2.3060
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1975 - loss: 2.3056
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1974 - loss: 2.3051
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1974 - loss: 2.3047 - val_accuracy: 0.2766 - val_loss: 2.0010
Epoch 16/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2188 - loss: 2.2548
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2061 - loss: 2.2843 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2035 - loss: 2.2865
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2015 - loss: 2.2896
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2015 - loss: 2.2897
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2022 - loss: 2.2872
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Epoch 17/110

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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2163 - loss: 2.2374
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Epoch 18/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2188 - loss: 2.0560
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2084 - loss: 2.2265 
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2143 - loss: 2.2191
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2151 - loss: 2.2171
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2156 - loss: 2.2161 - val_accuracy: 0.2648 - val_loss: 1.9406
Epoch 19/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2031 - loss: 2.0673
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1998 - loss: 2.1744 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2099 - loss: 2.1745
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[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2136 - loss: 2.1787
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2146 - loss: 2.1792
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2151 - loss: 2.1803
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2152 - loss: 2.1820
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2153 - loss: 2.1829 - val_accuracy: 0.2762 - val_loss: 1.9211
Epoch 20/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1719 - loss: 2.1247
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2099 - loss: 2.1671 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2160 - loss: 2.1537
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2174 - loss: 2.1547
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2189 - loss: 2.1547
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2199 - loss: 2.1549
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2205 - loss: 2.1556
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2208 - loss: 2.1562
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2210 - loss: 2.1564 - val_accuracy: 0.2735 - val_loss: 1.8987
Epoch 21/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 25ms/step - accuracy: 0.3438 - loss: 2.1162
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2380 - loss: 2.1692 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2301 - loss: 2.1652
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2242 - loss: 2.1601
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2241 - loss: 2.1573 - val_accuracy: 0.2773 - val_loss: 1.8856
Epoch 22/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2344 - loss: 2.2717
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2420 - loss: 2.1412 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2386 - loss: 2.1397
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[1m162/292[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2354 - loss: 2.1414
[1m203/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2347 - loss: 2.1429
[1m243/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2342 - loss: 2.1430
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2340 - loss: 2.1416 - val_accuracy: 0.2781 - val_loss: 1.8801
Epoch 23/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2500 - loss: 2.1439
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2302 - loss: 2.1204 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2359 - loss: 2.1155
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[1m145/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2392 - loss: 2.1122
[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2400 - loss: 2.1116
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2404 - loss: 2.1106
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2406 - loss: 2.1094
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2407 - loss: 2.1088 - val_accuracy: 0.2907 - val_loss: 1.8792
Epoch 24/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3125 - loss: 1.9846
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2629 - loss: 2.0376 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2538 - loss: 2.0557
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2501 - loss: 2.0660
[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2473 - loss: 2.0729
[1m197/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2452 - loss: 2.0771
[1m237/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2442 - loss: 2.0796
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2435 - loss: 2.0817
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2432 - loss: 2.0827 - val_accuracy: 0.3073 - val_loss: 1.8507
Epoch 25/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1875 - loss: 2.1452
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2280 - loss: 2.1102 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2348 - loss: 2.1008
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2389 - loss: 2.0952
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2418 - loss: 2.0927
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2434 - loss: 2.0908
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2447 - loss: 2.0894
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2456 - loss: 2.0882
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2461 - loss: 2.0875 - val_accuracy: 0.3056 - val_loss: 1.8480
Epoch 26/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2031 - loss: 1.9725
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2307 - loss: 2.1044 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2403 - loss: 2.0843
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2449 - loss: 2.0767
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2474 - loss: 2.0723
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2487 - loss: 2.0694
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2492 - loss: 2.0680
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2492 - loss: 2.0673
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2492 - loss: 2.0670 - val_accuracy: 0.3051 - val_loss: 1.8316
Epoch 27/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2574 - loss: 2.0521 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2564 - loss: 2.0604
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2567 - loss: 2.0613
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[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2570 - loss: 2.0589
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Epoch 28/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2455 - loss: 2.0839 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2480 - loss: 2.0653
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2519 - loss: 2.0550
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2526 - loss: 2.0522 - val_accuracy: 0.3184 - val_loss: 1.8183
Epoch 29/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2574 - loss: 2.0570 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2634 - loss: 2.0424
[1m119/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2639 - loss: 2.0392
[1m160/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2638 - loss: 2.0368
[1m199/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2634 - loss: 2.0352
[1m239/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2626 - loss: 2.0337
[1m279/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2620 - loss: 2.0327
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2619 - loss: 2.0325 - val_accuracy: 0.3097 - val_loss: 1.8216
Epoch 30/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2969 - loss: 1.7811
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2589 - loss: 1.9746 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2651 - loss: 1.9857
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2669 - loss: 1.9953
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2669 - loss: 2.0035
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2669 - loss: 2.0079
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2667 - loss: 2.0101
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2666 - loss: 2.0115
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2664 - loss: 2.0120 - val_accuracy: 0.3029 - val_loss: 1.8085
Epoch 31/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2969 - loss: 1.9702
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2718 - loss: 1.9790 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2692 - loss: 1.9881
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2682 - loss: 1.9944
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2686 - loss: 1.9982
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2690 - loss: 2.0007
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2689 - loss: 2.0025
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2684 - loss: 2.0043
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2680 - loss: 2.0053 - val_accuracy: 0.3408 - val_loss: 1.7992
Epoch 32/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2706 - loss: 1.9797 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2676 - loss: 1.9846
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2693 - loss: 1.9908
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Epoch 33/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2868 - loss: 1.9716 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2835 - loss: 1.9767
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Epoch 34/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2893 - loss: 1.9500 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2874 - loss: 1.9513
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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2828 - loss: 1.9617
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2827 - loss: 1.9636
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Epoch 35/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2800 - loss: 1.9743 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2807 - loss: 1.9744
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[1m183/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2841 - loss: 1.9664
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2841 - loss: 1.9656
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2839 - loss: 1.9650
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2838 - loss: 1.9645 - val_accuracy: 0.3330 - val_loss: 1.7713
Epoch 36/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3750 - loss: 1.9201
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2898 - loss: 1.9743 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2862 - loss: 1.9678
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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2843 - loss: 1.9663
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2842 - loss: 1.9664
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2840 - loss: 1.9667
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2838 - loss: 1.9665
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2837 - loss: 1.9662 - val_accuracy: 0.3343 - val_loss: 1.7630
Epoch 37/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2656 - loss: 1.8889
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2907 - loss: 1.9361 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2897 - loss: 1.9437
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2884 - loss: 1.9490
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2883 - loss: 1.9502
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Epoch 38/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2854 - loss: 1.9575 
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Epoch 39/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2911 - loss: 1.8935 
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Epoch 40/110

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Epoch 41/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3099 - loss: 1.9320 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3022 - loss: 1.9192
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[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2968 - loss: 1.9170
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2961 - loss: 1.9176 - val_accuracy: 0.3484 - val_loss: 1.7301
Epoch 42/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3273 - loss: 1.8787 
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3110 - loss: 1.9068
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Epoch 43/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3046 - loss: 1.9038 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3062 - loss: 1.9070
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Epoch 44/110

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Epoch 45/110

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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3046 - loss: 1.8939
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3051 - loss: 1.8926 - val_accuracy: 0.3567 - val_loss: 1.6980
Epoch 46/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1875 - loss: 2.0220
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3190 - loss: 1.8856 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3176 - loss: 1.8849
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3180 - loss: 1.8792
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3177 - loss: 1.8793
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3174 - loss: 1.8795 - val_accuracy: 0.3558 - val_loss: 1.6948
Epoch 47/110

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[1m 34/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3110 - loss: 1.8968 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3091 - loss: 1.8949
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[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3071 - loss: 1.8879
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3070 - loss: 1.8859
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3066 - loss: 1.8854
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3065 - loss: 1.8850
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3065 - loss: 1.8844 - val_accuracy: 0.3602 - val_loss: 1.6891
Epoch 48/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3179 - loss: 1.8777 
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3107 - loss: 1.8755
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3109 - loss: 1.8743
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Epoch 49/110

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Epoch 50/110

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[1m218/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3190 - loss: 1.8516
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Epoch 51/110

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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3184 - loss: 1.8527
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3184 - loss: 1.8529
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3183 - loss: 1.8528 - val_accuracy: 0.3541 - val_loss: 1.6751
Epoch 52/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 24ms/step - accuracy: 0.3125 - loss: 1.6783
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3186 - loss: 1.8343 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3211 - loss: 1.8422
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[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3223 - loss: 1.8390
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3226 - loss: 1.8395
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3227 - loss: 1.8401
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3227 - loss: 1.8409
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3227 - loss: 1.8415 - val_accuracy: 0.3526 - val_loss: 1.6675
Epoch 53/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3116 - loss: 1.8500 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3148 - loss: 1.8566
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3197 - loss: 1.8487
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Epoch 54/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3178 - loss: 1.8381 
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Epoch 55/110

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Epoch 56/110

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Epoch 57/110

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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3230 - loss: 1.8202 - val_accuracy: 0.3525 - val_loss: 1.6501
Epoch 58/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3278 - loss: 1.8453 
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3255 - loss: 1.8184
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3258 - loss: 1.8174
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Epoch 59/110

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Epoch 60/110

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Epoch 61/110

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Epoch 62/110

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Epoch 63/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3254 - loss: 1.8242 
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[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3342 - loss: 1.8022
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3342 - loss: 1.8024
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Epoch 64/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3317 - loss: 1.7650 
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Epoch 65/110

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Epoch 66/110

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Epoch 67/110

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Epoch 68/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3158 - loss: 1.8121 
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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3279 - loss: 1.7961
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3285 - loss: 1.7944
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3288 - loss: 1.7937 - val_accuracy: 0.3737 - val_loss: 1.6349
Epoch 69/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3341 - loss: 1.8025 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3390 - loss: 1.7876
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[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3432 - loss: 1.7786
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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3439 - loss: 1.7756
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Epoch 70/110

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Epoch 71/110

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Epoch 72/110

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Epoch 73/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3720 - loss: 1.7454 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3616 - loss: 1.7510
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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3503 - loss: 1.7564
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3496 - loss: 1.7566 - val_accuracy: 0.3706 - val_loss: 1.6166
Epoch 74/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3480 - loss: 1.7685 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3515 - loss: 1.7585
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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3550 - loss: 1.7523
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[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3548 - loss: 1.7511
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3544 - loss: 1.7514
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3541 - loss: 1.7513 - val_accuracy: 0.3771 - val_loss: 1.6128
Epoch 75/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3611 - loss: 1.7329 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3521 - loss: 1.7460
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[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3473 - loss: 1.7554
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Epoch 76/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3646 - loss: 1.7606 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3618 - loss: 1.7651
[1m107/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3579 - loss: 1.7689
[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3555 - loss: 1.7694
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3545 - loss: 1.7683
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3537 - loss: 1.7664
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3525 - loss: 1.7641 - val_accuracy: 0.3684 - val_loss: 1.6193
Epoch 77/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3438 - loss: 1.7670
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[1m147/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3475 - loss: 1.7476
[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3476 - loss: 1.7475
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3478 - loss: 1.7478
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3473 - loss: 1.7490
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Epoch 78/110

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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3560 - loss: 1.7314
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3561 - loss: 1.7316
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3561 - loss: 1.7321
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3561 - loss: 1.7330
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3561 - loss: 1.7334 - val_accuracy: 0.3748 - val_loss: 1.6252
Epoch 79/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3125 - loss: 2.0360
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3560 - loss: 1.7821 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3513 - loss: 1.7645
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3512 - loss: 1.7549
[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3517 - loss: 1.7498
[1m199/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3522 - loss: 1.7464
[1m240/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3524 - loss: 1.7444
[1m280/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3521 - loss: 1.7433
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3519 - loss: 1.7431 - val_accuracy: 0.3704 - val_loss: 1.6154

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:10[0m 830ms/step
[1m63/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 815us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 9ms/step
Saved model to disk.

Accuracy capturado en la ejecución 12: 37.82 [%]
F1-score capturado en la ejecución 12: 37.35 [%]

=== EJECUCIÓN 13 ===

--- TRAIN (ejecución 13) ---

--- TEST (ejecución 13) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18676, 6, 250)

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[1m143/584[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 714us/step
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[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 69/169[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 744us/step
[1m137/169[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 742us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 37.04 [%]
Global F1 score (validation) = 36.52 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[2.1130502e-01 2.1116844e-01 2.1187729e-01 ... 4.5546585e-06
  1.4889789e-01 9.6140979e-03]
 [2.1122852e-01 1.8252388e-01 2.1873286e-01 ... 1.0377733e-05
  1.4228164e-01 2.5732929e-02]
 [2.1206833e-01 1.9279766e-01 2.0385177e-01 ... 1.6215856e-05
  1.3432492e-01 2.9269114e-02]
 ...
 [2.0394638e-01 1.9126835e-01 2.1642944e-01 ... 6.4913627e-05
  1.4506158e-01 2.2223396e-02]
 [1.8767656e-01 2.1151690e-01 1.9901590e-01 ... 9.8416422e-05
  1.5963514e-01 6.0572927e-03]
 [5.8695532e-02 8.0045126e-02 5.9205145e-02 ... 8.1131159e-04
  7.4229047e-02 2.5072896e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 43.37 [%]
Global accuracy score (test) = 34.78 [%]
Global F1 score (train) = 42.06 [%]
Global F1 score (test) = 33.52 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.29      0.04      0.08       184
 CAMINAR CON MÓVIL O LIBRO       0.19      0.23      0.21       184
       CAMINAR USUAL SPEED       0.19      0.36      0.25       184
            CAMINAR ZIGZAG       0.15      0.16      0.16       184
          DE PIE BARRIENDO       0.20      0.39      0.26       184
   DE PIE DOBLANDO TOALLAS       0.12      0.05      0.07       184
    DE PIE MOVIENDO LIBROS       0.30      0.37      0.33       184
          DE PIE USANDO PC       0.36      0.54      0.43       184
        FASE REPOSO CON K5       0.55      0.86      0.67       184
INCREMENTAL CICLOERGOMETRO       0.86      0.60      0.71       184
           SENTADO LEYENDO       0.30      0.28      0.29       184
         SENTADO USANDO PC       0.29      0.10      0.15       184
      SENTADO VIENDO LA TV       0.43      0.43      0.43       184
   SUBIR Y BAJAR ESCALERAS       0.39      0.12      0.18       184
                    TROTAR       0.89      0.75      0.81       161

                  accuracy                           0.35      2737
                 macro avg       0.37      0.35      0.34      2737
              weighted avg       0.36      0.35      0.33      2737

2025-10-28 14:15:39.325225: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 14:15:39.336750: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1761657339.349957 2311010 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761657339.354243 2311010 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761657339.364171 2311010 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657339.364193 2311010 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657339.364195 2311010 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657339.364197 2311010 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 14:15:39.367443: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1761657341.742307 2311010 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13697 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/110
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761657343.458191 2311139 service.cc:152] XLA service 0x7b10ac00c5b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761657343.458253 2311139 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 14:15:43.495237: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
I0000 00:00:1761657343.659665 2311139 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761657345.929861 2311139 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16:46[0m 3s/step - accuracy: 0.0469 - loss: 3.6468
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[1m144/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0764 - loss: 3.5164
[1m180/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0775 - loss: 3.5000
[1m215/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0784 - loss: 3.4858
[1m254/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0792 - loss: 3.4709
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step - accuracy: 0.0803 - loss: 3.4572
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 16ms/step - accuracy: 0.0803 - loss: 3.4569 - val_accuracy: 0.1541 - val_loss: 2.4895
Epoch 2/110

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[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1029 - loss: 3.2368
[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1013 - loss: 3.2314
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Epoch 3/110

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Epoch 4/110

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Epoch 5/110

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Epoch 6/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1446 - loss: 2.7578 
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[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1402 - loss: 2.7506
[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1405 - loss: 2.7473
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1410 - loss: 2.7417 - val_accuracy: 0.2316 - val_loss: 2.2400
Epoch 7/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1434 - loss: 2.6890 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1454 - loss: 2.6776
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1492 - loss: 2.6583
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1496 - loss: 2.6554
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Epoch 8/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1659 - loss: 2.5457 
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1635 - loss: 2.5697
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1631 - loss: 2.5694
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1630 - loss: 2.5691 - val_accuracy: 0.2407 - val_loss: 2.1647
Epoch 9/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1562 - loss: 2.5235
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1560 - loss: 2.5568 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1620 - loss: 2.5416
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1661 - loss: 2.5319
[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1677 - loss: 2.5264
[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1688 - loss: 2.5226
[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1695 - loss: 2.5197
[1m259/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1696 - loss: 2.5178
[1m291/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1697 - loss: 2.5164
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1697 - loss: 2.5163 - val_accuracy: 0.2411 - val_loss: 2.1296
Epoch 10/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1875 - loss: 2.3798
[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1802 - loss: 2.4593 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1793 - loss: 2.4616
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1798 - loss: 2.4600
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1804 - loss: 2.4595
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1806 - loss: 2.4590
[1m222/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1810 - loss: 2.4580
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1813 - loss: 2.4567
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1815 - loss: 2.4556 - val_accuracy: 0.2522 - val_loss: 2.0915
Epoch 11/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2188 - loss: 2.4481
[1m 34/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1832 - loss: 2.3963 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1769 - loss: 2.4082
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1770 - loss: 2.4090
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1783 - loss: 2.4066
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1789 - loss: 2.4058
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1793 - loss: 2.4052
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1798 - loss: 2.4043
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1803 - loss: 2.4036 - val_accuracy: 0.2675 - val_loss: 2.0515
Epoch 12/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1406 - loss: 2.4502
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1899 - loss: 2.3693 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1918 - loss: 2.3651
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1934 - loss: 2.3637
[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1937 - loss: 2.3633
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1938 - loss: 2.3641
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1937 - loss: 2.3643
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1935 - loss: 2.3643
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1935 - loss: 2.3640 - val_accuracy: 0.2703 - val_loss: 2.0246
Epoch 13/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 26ms/step - accuracy: 0.1562 - loss: 2.3989
[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1967 - loss: 2.3294 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1970 - loss: 2.3289
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1982 - loss: 2.3279
[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1995 - loss: 2.3265
[1m197/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2000 - loss: 2.3265
[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2002 - loss: 2.3268
[1m277/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2002 - loss: 2.3267
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Epoch 14/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1875 - loss: 2.2991
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1968 - loss: 2.2806 
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Epoch 15/110

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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2082 - loss: 2.2553 - val_accuracy: 0.2747 - val_loss: 1.9424
Epoch 16/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2140 - loss: 2.2448 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2123 - loss: 2.2359
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[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2131 - loss: 2.2322
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2131 - loss: 2.2320
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2132 - loss: 2.2314
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2134 - loss: 2.2306 - val_accuracy: 0.2825 - val_loss: 1.9245
Epoch 17/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1875 - loss: 2.2140
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2108 - loss: 2.2236 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2124 - loss: 2.2228
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2139 - loss: 2.2171
[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2148 - loss: 2.2130
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2156 - loss: 2.2096
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2161 - loss: 2.2069
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2169 - loss: 2.2042
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2173 - loss: 2.2027 - val_accuracy: 0.2969 - val_loss: 1.9128
Epoch 18/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2812 - loss: 2.2268
[1m 34/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2280 - loss: 2.1612 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2279 - loss: 2.1666
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[1m146/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2247 - loss: 2.1773
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2243 - loss: 2.1786
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2244 - loss: 2.1784
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2243 - loss: 2.1779
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Epoch 19/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2187 - loss: 2.1549 
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[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2227 - loss: 2.1636
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2242 - loss: 2.1610
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2255 - loss: 2.1588
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2263 - loss: 2.1576
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2269 - loss: 2.1568 - val_accuracy: 0.3138 - val_loss: 1.8692
Epoch 20/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1562 - loss: 2.1239
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2188 - loss: 2.1576 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2274 - loss: 2.1504
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2298 - loss: 2.1477
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2312 - loss: 2.1456
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2323 - loss: 2.1443
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2331 - loss: 2.1435
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2338 - loss: 2.1422 - val_accuracy: 0.3271 - val_loss: 1.8658
Epoch 21/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2969 - loss: 2.1578
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2465 - loss: 2.1360 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2458 - loss: 2.1329
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2451 - loss: 2.1312
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2447 - loss: 2.1294
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2444 - loss: 2.1272
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2440 - loss: 2.1254
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2436 - loss: 2.1241
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2433 - loss: 2.1234 - val_accuracy: 0.3251 - val_loss: 1.8452
Epoch 22/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2031 - loss: 2.2251
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2606 - loss: 2.0743 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2527 - loss: 2.0876
[1m122/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2488 - loss: 2.0928
[1m163/292[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2478 - loss: 2.0936
[1m204/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2472 - loss: 2.0947
[1m242/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2467 - loss: 2.0954
[1m283/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2461 - loss: 2.0958
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2460 - loss: 2.0958 - val_accuracy: 0.3171 - val_loss: 1.8370
Epoch 23/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2812 - loss: 2.0407
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2606 - loss: 2.0840 
[1m 82/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2568 - loss: 2.0862
[1m121/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2545 - loss: 2.0877
[1m162/292[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2540 - loss: 2.0872
[1m203/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2533 - loss: 2.0871
[1m243/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2527 - loss: 2.0873
[1m284/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2525 - loss: 2.0869
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2525 - loss: 2.0868 - val_accuracy: 0.3190 - val_loss: 1.8329
Epoch 24/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2656 - loss: 2.2133
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2376 - loss: 2.1277 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2403 - loss: 2.1143
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2421 - loss: 2.1054
[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2427 - loss: 2.0992
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2431 - loss: 2.0953
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2440 - loss: 2.0921
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2449 - loss: 2.0893
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Epoch 25/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2344 - loss: 2.0758
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2536 - loss: 2.0648 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2561 - loss: 2.0623
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[1m258/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2576 - loss: 2.0562
[1m290/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2577 - loss: 2.0561
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2577 - loss: 2.0561 - val_accuracy: 0.3197 - val_loss: 1.7980
Epoch 26/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2656 - loss: 2.2289
[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2724 - loss: 2.0583 
[1m 82/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2679 - loss: 2.0480
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2648 - loss: 2.0405
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2643 - loss: 2.0385
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2642 - loss: 2.0381
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2641 - loss: 2.0378
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2640 - loss: 2.0377 - val_accuracy: 0.3312 - val_loss: 1.7751
Epoch 27/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.2188 - loss: 1.9229
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2626 - loss: 2.0144 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2627 - loss: 2.0139
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[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2628 - loss: 2.0172
[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2632 - loss: 2.0174
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2634 - loss: 2.0176
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2638 - loss: 2.0178
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2641 - loss: 2.0180 - val_accuracy: 0.3410 - val_loss: 1.7734
Epoch 28/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2812 - loss: 2.1073
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2795 - loss: 1.9968 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2771 - loss: 2.0076
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2768 - loss: 2.0113
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2767 - loss: 2.0126
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2763 - loss: 2.0135
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2761 - loss: 2.0135
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2759 - loss: 2.0138
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2758 - loss: 2.0138 - val_accuracy: 0.3275 - val_loss: 1.7644
Epoch 29/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2812 - loss: 1.9693
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2684 - loss: 2.0086 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2746 - loss: 2.0038
[1m120/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2767 - loss: 2.0027
[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2770 - loss: 2.0024
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2775 - loss: 2.0020
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2777 - loss: 2.0015
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2780 - loss: 2.0009
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2780 - loss: 2.0005 - val_accuracy: 0.3443 - val_loss: 1.7476
Epoch 30/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3281 - loss: 1.9832
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2798 - loss: 2.0010 
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Epoch 31/110

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Epoch 32/110

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Epoch 33/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2994 - loss: 1.9101 
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[1m216/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2935 - loss: 1.9474
[1m254/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2931 - loss: 1.9485
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.2928 - loss: 1.9493
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2928 - loss: 1.9494 - val_accuracy: 0.3478 - val_loss: 1.7142
Epoch 34/110

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[1m 42/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3034 - loss: 1.9399 
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[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2957 - loss: 1.9341
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2945 - loss: 1.9360
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Epoch 35/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3261 - loss: 1.9112 
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[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3104 - loss: 1.9314
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Epoch 36/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2664 - loss: 1.9151 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2831 - loss: 1.9096
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2964 - loss: 1.9097
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2978 - loss: 1.9098
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Epoch 37/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.4219 - loss: 1.8871
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3155 - loss: 1.9333 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3094 - loss: 1.9322
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[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3075 - loss: 1.9171
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3068 - loss: 1.9160 - val_accuracy: 0.3602 - val_loss: 1.6867
Epoch 38/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2656 - loss: 2.0488
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2962 - loss: 1.9250 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2999 - loss: 1.9253
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3024 - loss: 1.9217
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3029 - loss: 1.9201
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3041 - loss: 1.9176
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3050 - loss: 1.9157
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3055 - loss: 1.9147 - val_accuracy: 0.3774 - val_loss: 1.6879
Epoch 39/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2812 - loss: 2.0585
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3175 - loss: 1.9162 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3206 - loss: 1.9007
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3185 - loss: 1.8987
[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3167 - loss: 1.8990
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3157 - loss: 1.8995
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3149 - loss: 1.8996
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3141 - loss: 1.8997
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3136 - loss: 1.8998 - val_accuracy: 0.3678 - val_loss: 1.6716
Epoch 40/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3594 - loss: 2.1061
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3013 - loss: 1.9025 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2994 - loss: 1.9014
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[1m147/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3044 - loss: 1.8871
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3051 - loss: 1.8852
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3055 - loss: 1.8845
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3055 - loss: 1.8847
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3055 - loss: 1.8849 - val_accuracy: 0.3571 - val_loss: 1.6733
Epoch 41/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2970 - loss: 1.9230 
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Epoch 42/110

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Epoch 43/110

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[1m290/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3137 - loss: 1.8841
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Epoch 44/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3223 - loss: 1.8348 
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[1m199/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3196 - loss: 1.8526
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3194 - loss: 1.8539
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3191 - loss: 1.8547
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3190 - loss: 1.8550 - val_accuracy: 0.3713 - val_loss: 1.6512
Epoch 45/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3181 - loss: 1.8822 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3194 - loss: 1.8774
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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3209 - loss: 1.8638
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Epoch 46/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3130 - loss: 1.8688 
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Epoch 47/110

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Epoch 48/110

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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3229 - loss: 1.8353
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Epoch 49/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3294 - loss: 1.8128 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3255 - loss: 1.8217
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[1m242/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3244 - loss: 1.8312
[1m284/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3243 - loss: 1.8325
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3243 - loss: 1.8327 - val_accuracy: 0.3652 - val_loss: 1.6296
Epoch 50/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3295 - loss: 1.8067 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3316 - loss: 1.8097
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[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3294 - loss: 1.8189
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3292 - loss: 1.8204
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3291 - loss: 1.8209
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3295 - loss: 1.8213
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3298 - loss: 1.8216 - val_accuracy: 0.3652 - val_loss: 1.6275
Epoch 51/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3077 - loss: 1.8282 
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[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3183 - loss: 1.8349
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3195 - loss: 1.8345
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3203 - loss: 1.8342
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Epoch 52/110

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Epoch 53/110

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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3321 - loss: 1.8203
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Epoch 54/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3458 - loss: 1.8380 
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[1m219/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3346 - loss: 1.8232
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3345 - loss: 1.8214 - val_accuracy: 0.3750 - val_loss: 1.6268
Epoch 55/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3254 - loss: 1.7635 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3237 - loss: 1.7757
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[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3245 - loss: 1.7868
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3252 - loss: 1.7900
[1m222/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3258 - loss: 1.7925
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3263 - loss: 1.7946
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3268 - loss: 1.7955 - val_accuracy: 0.3748 - val_loss: 1.6184
Epoch 56/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2969 - loss: 1.8629
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3361 - loss: 1.7750 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3411 - loss: 1.7783
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3404 - loss: 1.7828
[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3394 - loss: 1.7860
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3390 - loss: 1.7885
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3386 - loss: 1.7902
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3382 - loss: 1.7912
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Epoch 57/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3216 - loss: 1.8121 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3249 - loss: 1.8065
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Epoch 58/110

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Epoch 59/110

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Epoch 60/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3467 - loss: 1.7382 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3444 - loss: 1.7491
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[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3417 - loss: 1.7721
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3409 - loss: 1.7746
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3405 - loss: 1.7757 - val_accuracy: 0.3821 - val_loss: 1.6260
Epoch 61/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3411 - loss: 1.7885 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3392 - loss: 1.7925
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[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3419 - loss: 1.7847
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3415 - loss: 1.7842
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3411 - loss: 1.7841
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3409 - loss: 1.7839 - val_accuracy: 0.3883 - val_loss: 1.6186
Epoch 62/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3460 - loss: 1.7768 
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[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3397 - loss: 1.7777
[1m258/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3395 - loss: 1.7782
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Epoch 63/110

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Epoch 64/110

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Epoch 65/110

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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3397 - loss: 1.7752
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3397 - loss: 1.7746
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Epoch 66/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3397 - loss: 1.7534 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3417 - loss: 1.7549
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[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3400 - loss: 1.7626
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3398 - loss: 1.7649
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3397 - loss: 1.7662
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Epoch 67/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3366 - loss: 1.7785 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3433 - loss: 1.7725
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Epoch 68/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3517 - loss: 1.8033 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3532 - loss: 1.7833
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Epoch 69/110

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Epoch 70/110

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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3500 - loss: 1.7533
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3490 - loss: 1.7539 - val_accuracy: 0.3870 - val_loss: 1.6109
Epoch 71/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3281 - loss: 1.7639
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3510 - loss: 1.7868 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3539 - loss: 1.7773
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[1m181/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3487 - loss: 1.7693
[1m218/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3479 - loss: 1.7670
[1m258/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3473 - loss: 1.7651
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3470 - loss: 1.7637 - val_accuracy: 0.3996 - val_loss: 1.6071
Epoch 72/110

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[1m 34/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3422 - loss: 1.8057 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3437 - loss: 1.7764
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3435 - loss: 1.7571
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3437 - loss: 1.7539
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3440 - loss: 1.7513
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3441 - loss: 1.7498
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3440 - loss: 1.7492 - val_accuracy: 0.3776 - val_loss: 1.6036
Epoch 73/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3768 - loss: 1.6956 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3661 - loss: 1.7230
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[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3586 - loss: 1.7340
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3566 - loss: 1.7343
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Epoch 74/110

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[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3423 - loss: 1.7507
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Epoch 75/110

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[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3492 - loss: 1.7272
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Epoch 76/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3617 - loss: 1.7581 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3603 - loss: 1.7467
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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3557 - loss: 1.7386
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3551 - loss: 1.7385
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3548 - loss: 1.7384 - val_accuracy: 0.3895 - val_loss: 1.6023
Epoch 77/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 25ms/step - accuracy: 0.4375 - loss: 1.7271
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3612 - loss: 1.7370 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3577 - loss: 1.7349
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3551 - loss: 1.7397
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3534 - loss: 1.7422
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3522 - loss: 1.7428
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3518 - loss: 1.7427
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3515 - loss: 1.7422
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3513 - loss: 1.7419 - val_accuracy: 0.3865 - val_loss: 1.5976
Epoch 78/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3279 - loss: 1.7437 
[1m 82/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3342 - loss: 1.7472
[1m121/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3365 - loss: 1.7491
[1m163/292[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3388 - loss: 1.7489
[1m202/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3404 - loss: 1.7482
[1m243/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3418 - loss: 1.7471
[1m284/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3429 - loss: 1.7454
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Epoch 79/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3552 - loss: 1.7059 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3553 - loss: 1.7034
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3545 - loss: 1.7058
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3519 - loss: 1.7132
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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:15[0m 885ms/step
[1m69/86[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 742us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
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Saved model to disk.

Accuracy capturado en la ejecución 13: 34.78 [%]
F1-score capturado en la ejecución 13: 33.52 [%]

=== EJECUCIÓN 14 ===

--- TRAIN (ejecución 14) ---

--- TEST (ejecución 14) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18676, 6, 250)

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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
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[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 8ms/step

[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 64/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 796us/step
[1m133/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 760us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 38.39 [%]
Global F1 score (validation) = 38.02 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[2.07382157e-01 2.11586177e-01 2.04871595e-01 ... 7.96413769e-06
  1.54998824e-01 6.05603307e-03]
 [2.13746548e-01 1.94457307e-01 2.16548428e-01 ... 4.02700880e-06
  1.42744556e-01 1.07045500e-02]
 [2.14912131e-01 1.96355596e-01 2.14920223e-01 ... 5.33592765e-06
  1.47997692e-01 1.03496816e-02]
 ...
 [1.99517936e-01 2.11537540e-01 2.09682211e-01 ... 7.89724800e-05
  1.60649508e-01 1.34224826e-02]
 [1.85499892e-01 2.09320426e-01 1.96154699e-01 ... 1.58694384e-04
  1.74577683e-01 7.56786531e-03]
 [7.94981793e-02 9.77603570e-02 8.06530342e-02 ... 1.73835189e-03
  1.07485384e-01 4.27777786e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 43.63 [%]
Global accuracy score (test) = 34.2 [%]
Global F1 score (train) = 42.74 [%]
Global F1 score (test) = 33.63 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.23      0.37      0.29       184
 CAMINAR CON MÓVIL O LIBRO       0.17      0.24      0.20       184
       CAMINAR USUAL SPEED       0.23      0.12      0.16       184
            CAMINAR ZIGZAG       0.06      0.05      0.05       184
          DE PIE BARRIENDO       0.21      0.42      0.28       184
   DE PIE DOBLANDO TOALLAS       0.30      0.11      0.17       184
    DE PIE MOVIENDO LIBROS       0.27      0.35      0.30       184
          DE PIE USANDO PC       0.42      0.62      0.50       184
        FASE REPOSO CON K5       0.71      0.83      0.77       184
INCREMENTAL CICLOERGOMETRO       0.83      0.58      0.68       184
           SENTADO LEYENDO       0.37      0.35      0.36       184
         SENTADO USANDO PC       0.18      0.11      0.14       184
      SENTADO VIENDO LA TV       0.32      0.27      0.29       184
   SUBIR Y BAJAR ESCALERAS       0.23      0.08      0.12       184
                    TROTAR       0.85      0.65      0.73       161

                  accuracy                           0.34      2737
                 macro avg       0.36      0.34      0.34      2737
              weighted avg       0.35      0.34      0.33      2737

2025-10-28 14:16:46.112064: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 14:16:46.123574: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1761657406.136887 2319388 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761657406.141250 2319388 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761657406.151243 2319388 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657406.151266 2319388 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657406.151268 2319388 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657406.151270 2319388 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 14:16:46.154600: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1761657408.518112 2319388 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13697 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/110
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761657410.215275 2319522 service.cc:152] XLA service 0x709d78004380 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761657410.215313 2319522 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 14:16:50.248665: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
I0000 00:00:1761657410.419161 2319522 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761657412.687830 2319522 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/110

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Epoch 3/110

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[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1143 - loss: 3.0343
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Epoch 4/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1314 - loss: 2.8973 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1322 - loss: 2.8983
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[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1316 - loss: 2.8945
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1310 - loss: 2.8942
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1305 - loss: 2.8943
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1304 - loss: 2.8942 - val_accuracy: 0.1859 - val_loss: 2.3353
Epoch 5/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1238 - loss: 2.8493 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1267 - loss: 2.8491
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1291 - loss: 2.8389
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Epoch 6/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1350 - loss: 2.7636 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1354 - loss: 2.7633
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[1m160/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1366 - loss: 2.7549
[1m199/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1371 - loss: 2.7507
[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1376 - loss: 2.7466
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1381 - loss: 2.7435
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1384 - loss: 2.7414 - val_accuracy: 0.2187 - val_loss: 2.2819
Epoch 7/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2188 - loss: 2.5393
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1637 - loss: 2.6421 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1585 - loss: 2.6485
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1559 - loss: 2.6514
[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1542 - loss: 2.6521
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1531 - loss: 2.6525
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1522 - loss: 2.6527
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1516 - loss: 2.6523
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1514 - loss: 2.6521 - val_accuracy: 0.2209 - val_loss: 2.2539
Epoch 8/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 26ms/step - accuracy: 0.1094 - loss: 2.5619
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1584 - loss: 2.6024 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1615 - loss: 2.5952
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[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1627 - loss: 2.5905
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1624 - loss: 2.5889
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1622 - loss: 2.5878
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1623 - loss: 2.5865
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1624 - loss: 2.5858 - val_accuracy: 0.2194 - val_loss: 2.2398
Epoch 9/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.2344 - loss: 2.4019
[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1723 - loss: 2.5648 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1676 - loss: 2.5699
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[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1666 - loss: 2.5625
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1665 - loss: 2.5588
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1667 - loss: 2.5556
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1668 - loss: 2.5529
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1669 - loss: 2.5508 - val_accuracy: 0.2194 - val_loss: 2.2058
Epoch 10/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2031 - loss: 2.5118
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1733 - loss: 2.5012 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1718 - loss: 2.5027
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1714 - loss: 2.5029
[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1708 - loss: 2.5028
[1m198/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1708 - loss: 2.5009
[1m240/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1710 - loss: 2.4987
[1m282/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1711 - loss: 2.4964
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1711 - loss: 2.4959 - val_accuracy: 0.2209 - val_loss: 2.1624
Epoch 11/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2812 - loss: 2.3769
[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1989 - loss: 2.4325 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1934 - loss: 2.4393
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1897 - loss: 2.4435
[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1880 - loss: 2.4442
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1869 - loss: 2.4436
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1861 - loss: 2.4426
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1857 - loss: 2.4416
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Epoch 12/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1875 - loss: 2.2899
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1971 - loss: 2.3408 
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1858 - loss: 2.3871
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Epoch 13/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1094 - loss: 2.3979
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1828 - loss: 2.3688 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1867 - loss: 2.3734
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[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1893 - loss: 2.3717
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1898 - loss: 2.3695
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1901 - loss: 2.3679
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1904 - loss: 2.3675
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1907 - loss: 2.3665 - val_accuracy: 0.2418 - val_loss: 2.0621
Epoch 14/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.2500 - loss: 2.3507
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1936 - loss: 2.3303 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1907 - loss: 2.3310
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[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1924 - loss: 2.3316
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1935 - loss: 2.3302
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1945 - loss: 2.3284
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1951 - loss: 2.3268
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1954 - loss: 2.3259 - val_accuracy: 0.2511 - val_loss: 2.0316
Epoch 15/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2344 - loss: 2.2204
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2165 - loss: 2.2842 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2139 - loss: 2.2816
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2118 - loss: 2.2808
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2107 - loss: 2.2803
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2099 - loss: 2.2797
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2096 - loss: 2.2785
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2093 - loss: 2.2777
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2091 - loss: 2.2775 - val_accuracy: 0.2588 - val_loss: 2.0084
Epoch 16/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2500 - loss: 2.1934
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1973 - loss: 2.3044 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1999 - loss: 2.2959
[1m119/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2029 - loss: 2.2874
[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2049 - loss: 2.2814
[1m200/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2060 - loss: 2.2776
[1m239/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2066 - loss: 2.2748
[1m277/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2067 - loss: 2.2730
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Epoch 17/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 25ms/step - accuracy: 0.1719 - loss: 2.2525
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2016 - loss: 2.2449 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1994 - loss: 2.2505
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[1m146/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2031 - loss: 2.2453
[1m181/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2047 - loss: 2.2426
[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2062 - loss: 2.2402
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2075 - loss: 2.2387
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2084 - loss: 2.2376 - val_accuracy: 0.2792 - val_loss: 1.9669
Epoch 18/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2656 - loss: 2.1294
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2330 - loss: 2.2062 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2329 - loss: 2.1991
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[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2302 - loss: 2.1982
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2295 - loss: 2.1984
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2288 - loss: 2.1987
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2281 - loss: 2.1992
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2277 - loss: 2.1995 - val_accuracy: 0.2829 - val_loss: 1.9463
Epoch 19/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1562 - loss: 2.1938
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2386 - loss: 2.1479 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2333 - loss: 2.1576
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[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2302 - loss: 2.1667
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2299 - loss: 2.1682
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2296 - loss: 2.1688
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2292 - loss: 2.1695
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2290 - loss: 2.1701 - val_accuracy: 0.3040 - val_loss: 1.9133
Epoch 20/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1719 - loss: 2.3468
[1m 33/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2271 - loss: 2.2007 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2265 - loss: 2.1850
[1m111/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2261 - loss: 2.1773
[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2250 - loss: 2.1751
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2243 - loss: 2.1743
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2239 - loss: 2.1736
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2241 - loss: 2.1721
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2243 - loss: 2.1708 - val_accuracy: 0.2988 - val_loss: 1.9025
Epoch 21/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2500 - loss: 2.1927
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2340 - loss: 2.1428 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2321 - loss: 2.1414
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2312 - loss: 2.1422
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2308 - loss: 2.1434
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2309 - loss: 2.1436
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2312 - loss: 2.1433
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2315 - loss: 2.1428
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2317 - loss: 2.1425 - val_accuracy: 0.2973 - val_loss: 1.8909
Epoch 22/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.2031 - loss: 2.3111
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2445 - loss: 2.1302 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2423 - loss: 2.1277
[1m120/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2406 - loss: 2.1271
[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2396 - loss: 2.1263
[1m198/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2386 - loss: 2.1266
[1m239/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2377 - loss: 2.1270
[1m277/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2369 - loss: 2.1276
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Epoch 23/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2969 - loss: 2.0608
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2281 - loss: 2.1409 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2262 - loss: 2.1326
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[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2295 - loss: 2.1265
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Epoch 24/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2188 - loss: 2.1322
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2487 - loss: 2.0823 
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2433 - loss: 2.0997
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2432 - loss: 2.0988
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2431 - loss: 2.0985 - val_accuracy: 0.2999 - val_loss: 1.8556
Epoch 25/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1719 - loss: 2.1383
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2313 - loss: 2.0979 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2324 - loss: 2.0973
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[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2357 - loss: 2.0907
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2368 - loss: 2.0902
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2379 - loss: 2.0894
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2385 - loss: 2.0887 - val_accuracy: 0.3180 - val_loss: 1.8390
Epoch 26/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2500 - loss: 2.0341
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2415 - loss: 2.0504 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2429 - loss: 2.0580
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2444 - loss: 2.0633
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2449 - loss: 2.0653
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2455 - loss: 2.0655
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2462 - loss: 2.0652
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2468 - loss: 2.0648
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2471 - loss: 2.0646 - val_accuracy: 0.3129 - val_loss: 1.8338
Epoch 27/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.1406 - loss: 2.2886
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2332 - loss: 2.0686 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2403 - loss: 2.0603
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2466 - loss: 2.0550
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2479 - loss: 2.0531
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2487 - loss: 2.0526
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2492 - loss: 2.0528
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2497 - loss: 2.0525 - val_accuracy: 0.3042 - val_loss: 1.8225
Epoch 28/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2812 - loss: 1.9396
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2686 - loss: 2.0463 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2657 - loss: 2.0487
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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2628 - loss: 2.0504
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2622 - loss: 2.0490
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2616 - loss: 2.0479
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2614 - loss: 2.0471
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2611 - loss: 2.0470 - val_accuracy: 0.3273 - val_loss: 1.8138
Epoch 29/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.2344 - loss: 1.9436
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2659 - loss: 2.0112 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2631 - loss: 2.0216
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2617 - loss: 2.0288
[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2613 - loss: 2.0336
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2611 - loss: 2.0363
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2609 - loss: 2.0378
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2610 - loss: 2.0379
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2611 - loss: 2.0377 - val_accuracy: 0.3129 - val_loss: 1.8007
Epoch 30/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2656 - loss: 2.0077
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2675 - loss: 2.0194 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2644 - loss: 2.0216
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2642 - loss: 2.0209
[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2641 - loss: 2.0195
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2642 - loss: 2.0190
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2642 - loss: 2.0190
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2644 - loss: 2.0188
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2644 - loss: 2.0189 - val_accuracy: 0.3169 - val_loss: 1.7971
Epoch 31/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 25ms/step - accuracy: 0.2969 - loss: 1.8540
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2663 - loss: 1.9864 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2669 - loss: 1.9992
[1m107/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2670 - loss: 2.0034
[1m144/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2666 - loss: 2.0059
[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2665 - loss: 2.0076
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2668 - loss: 2.0085
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2671 - loss: 2.0090
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2672 - loss: 2.0091 - val_accuracy: 0.3114 - val_loss: 1.7923
Epoch 32/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.3281 - loss: 1.8579
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2823 - loss: 1.9788 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2781 - loss: 1.9843
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2743 - loss: 1.9865
[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2728 - loss: 1.9890
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2713 - loss: 1.9916
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2702 - loss: 1.9945
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2694 - loss: 1.9966
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2690 - loss: 1.9980 - val_accuracy: 0.3234 - val_loss: 1.7778
Epoch 33/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3125 - loss: 1.7943
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2843 - loss: 1.9206 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2810 - loss: 1.9538
[1m121/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2805 - loss: 1.9657
[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2791 - loss: 1.9732
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2780 - loss: 1.9778
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2775 - loss: 1.9797
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2769 - loss: 1.9819
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Epoch 34/110

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Epoch 35/110

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Epoch 36/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2578 - loss: 1.9504 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2693 - loss: 1.9412
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2776 - loss: 1.9553
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2786 - loss: 1.9568 - val_accuracy: 0.3304 - val_loss: 1.7433
Epoch 37/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2726 - loss: 1.9444 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2801 - loss: 1.9425
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[1m182/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2839 - loss: 1.9486
[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2847 - loss: 1.9496
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2855 - loss: 1.9510 - val_accuracy: 0.3439 - val_loss: 1.7461
Epoch 38/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2795 - loss: 1.9811 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2894 - loss: 1.9639
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2934 - loss: 1.9532
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Epoch 39/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2951 - loss: 1.9178 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2906 - loss: 1.9382
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Epoch 40/110

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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2929 - loss: 1.9324
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Epoch 41/110

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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2880 - loss: 1.9247
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Epoch 42/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2812 - loss: 2.0322
[1m 34/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3171 - loss: 1.9562 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3094 - loss: 1.9437
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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3054 - loss: 1.9293
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3050 - loss: 1.9278
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3047 - loss: 1.9271 - val_accuracy: 0.3364 - val_loss: 1.7128
Epoch 43/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2812 - loss: 1.9046
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2987 - loss: 1.9234 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3041 - loss: 1.9137
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[1m160/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3057 - loss: 1.9110
[1m201/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3063 - loss: 1.9097
[1m240/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3060 - loss: 1.9099
[1m279/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3058 - loss: 1.9096
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3058 - loss: 1.9094 - val_accuracy: 0.3495 - val_loss: 1.7014
Epoch 44/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2997 - loss: 1.8906 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3025 - loss: 1.8990
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[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3023 - loss: 1.8992
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3022 - loss: 1.9005
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3023 - loss: 1.9015
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Epoch 45/110

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Epoch 46/110

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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3155 - loss: 1.9023
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Epoch 47/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3099 - loss: 1.8924 
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[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3157 - loss: 1.8765
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3161 - loss: 1.8758 - val_accuracy: 0.3580 - val_loss: 1.6873
Epoch 48/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3438 - loss: 1.7719
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3271 - loss: 1.8577 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3182 - loss: 1.8733
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[1m161/292[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3140 - loss: 1.8844
[1m202/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3123 - loss: 1.8868
[1m241/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3115 - loss: 1.8870
[1m282/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3113 - loss: 1.8864
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3113 - loss: 1.8862 - val_accuracy: 0.3562 - val_loss: 1.6746
Epoch 49/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2656 - loss: 1.7781
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3231 - loss: 1.8425 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3225 - loss: 1.8525
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[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3213 - loss: 1.8595
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3209 - loss: 1.8621
[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3201 - loss: 1.8630
[1m278/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3195 - loss: 1.8638
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Epoch 50/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2934 - loss: 1.8657 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3018 - loss: 1.8638
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3104 - loss: 1.8695
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Epoch 51/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3278 - loss: 1.8257 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3235 - loss: 1.8436
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Epoch 52/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 26ms/step - accuracy: 0.3281 - loss: 1.7828
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[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3188 - loss: 1.8418
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3184 - loss: 1.8485
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3182 - loss: 1.8497
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3182 - loss: 1.8501 - val_accuracy: 0.3654 - val_loss: 1.6559
Epoch 53/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2969 - loss: 1.8825
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3254 - loss: 1.8439 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3214 - loss: 1.8499
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[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3188 - loss: 1.8504
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3186 - loss: 1.8498
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3186 - loss: 1.8492
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3187 - loss: 1.8487 - val_accuracy: 0.3711 - val_loss: 1.6604
Epoch 54/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3594 - loss: 1.8088
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3203 - loss: 1.8756 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3192 - loss: 1.8691
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[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3215 - loss: 1.8579
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3220 - loss: 1.8553
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3221 - loss: 1.8535
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3222 - loss: 1.8522
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3222 - loss: 1.8518 - val_accuracy: 0.3565 - val_loss: 1.6506
Epoch 55/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.2656 - loss: 1.7878
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3007 - loss: 1.8424 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3084 - loss: 1.8415
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3125 - loss: 1.8403
[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3147 - loss: 1.8391
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[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3170 - loss: 1.8383
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3181 - loss: 1.8375
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Epoch 56/110

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Epoch 57/110

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Epoch 58/110

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Epoch 59/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3582 - loss: 1.7870 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3512 - loss: 1.8024
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[1m290/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3383 - loss: 1.8157
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Epoch 60/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3206 - loss: 1.8649 
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Epoch 61/110

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Epoch 62/110

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Epoch 63/110

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Epoch 64/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3527 - loss: 1.8201 
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3459 - loss: 1.7999
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3448 - loss: 1.7999
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3443 - loss: 1.7998 - val_accuracy: 0.3735 - val_loss: 1.6215
Epoch 65/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3326 - loss: 1.7862 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3326 - loss: 1.7791
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[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3323 - loss: 1.7882
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Epoch 66/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3528 - loss: 1.7949 
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Epoch 67/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3476 - loss: 1.7772 
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Epoch 68/110

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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3396 - loss: 1.7842
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Epoch 69/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3594 - loss: 1.6563
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3369 - loss: 1.7766 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3379 - loss: 1.7713
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[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3386 - loss: 1.7751
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3392 - loss: 1.7761
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3400 - loss: 1.7767
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3403 - loss: 1.7772 - val_accuracy: 0.3682 - val_loss: 1.6186
Epoch 70/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3267 - loss: 1.7957 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3345 - loss: 1.7910
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3373 - loss: 1.7881
[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3387 - loss: 1.7865
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3399 - loss: 1.7843
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3406 - loss: 1.7828
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3411 - loss: 1.7816
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3413 - loss: 1.7811 - val_accuracy: 0.3704 - val_loss: 1.6141
Epoch 71/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3634 - loss: 1.7481 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3573 - loss: 1.7592
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[1m241/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3498 - loss: 1.7674
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Epoch 72/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3280 - loss: 1.7793 
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[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3395 - loss: 1.7684
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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:11[0m 847ms/step
[1m68/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 753us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
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Saved model to disk.

Accuracy capturado en la ejecución 14: 34.2 [%]
F1-score capturado en la ejecución 14: 33.63 [%]

=== EJECUCIÓN 15 ===

--- TRAIN (ejecución 15) ---

--- TEST (ejecución 15) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18676, 6, 250)

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[1m 60/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 854us/step  
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m58/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 880us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 63/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 809us/step
[1m136/169[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 744us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 36.48 [%]
Global F1 score (validation) = 35.02 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[2.1337779e-01 1.8245330e-01 1.9672932e-01 ... 5.3945560e-06
  1.5781610e-01 4.8380308e-02]
 [2.0924072e-01 2.0965818e-01 2.0349593e-01 ... 1.2686466e-05
  1.5150020e-01 1.2080924e-02]
 [1.6266230e-01 1.9212924e-01 1.7085518e-01 ... 1.0842007e-04
  2.0702539e-01 2.5711682e-02]
 ...
 [1.9456005e-01 1.8328805e-01 1.8996948e-01 ... 7.2236631e-05
  1.6979910e-01 4.9719654e-02]
 [1.6951199e-01 2.2009793e-01 1.9140060e-01 ... 6.8647270e-05
  1.8736596e-01 1.0780588e-02]
 [7.7116542e-02 1.1212079e-01 8.9686602e-02 ... 2.4779886e-03
  9.9487104e-02 4.5668501e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 42.53 [%]
Global accuracy score (test) = 35.51 [%]
Global F1 score (train) = 40.51 [%]
Global F1 score (test) = 33.59 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.29      0.41      0.34       184
 CAMINAR CON MÓVIL O LIBRO       0.22      0.50      0.30       184
       CAMINAR USUAL SPEED       0.00      0.00      0.00       184
            CAMINAR ZIGZAG       0.09      0.07      0.08       184
          DE PIE BARRIENDO       0.26      0.39      0.31       184
   DE PIE DOBLANDO TOALLAS       0.34      0.18      0.23       184
    DE PIE MOVIENDO LIBROS       0.27      0.34      0.30       184
          DE PIE USANDO PC       0.43      0.61      0.51       184
        FASE REPOSO CON K5       0.51      0.81      0.62       184
INCREMENTAL CICLOERGOMETRO       0.92      0.60      0.72       184
           SENTADO LEYENDO       0.24      0.25      0.25       184
         SENTADO USANDO PC       0.17      0.07      0.09       184
      SENTADO VIENDO LA TV       0.37      0.32      0.34       184
   SUBIR Y BAJAR ESCALERAS       0.24      0.09      0.13       184
                    TROTAR       0.88      0.75      0.81       161

                  accuracy                           0.36      2737
                 macro avg       0.35      0.36      0.34      2737
              weighted avg       0.34      0.36      0.33      2737

2025-10-28 14:17:49.167799: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 14:17:49.179234: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1761657469.192722 2327148 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761657469.196955 2327148 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761657469.206708 2327148 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657469.206727 2327148 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657469.206730 2327148 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657469.206731 2327148 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 14:17:49.209894: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1761657471.587819 2327148 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13697 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/110
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761657473.288515 2327246 service.cc:152] XLA service 0x7253fc00c5d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761657473.288553 2327246 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 14:17:53.321079: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
I0000 00:00:1761657473.484538 2327246 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761657475.779760 2327246 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/110

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Epoch 3/110

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[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1131 - loss: 3.0520
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[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1139 - loss: 3.0458
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Epoch 4/110

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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1227 - loss: 2.9242
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1233 - loss: 2.9202
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Epoch 5/110

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Epoch 6/110

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Epoch 7/110

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Epoch 8/110

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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1586 - loss: 2.5963 - val_accuracy: 0.2250 - val_loss: 2.2192
Epoch 9/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1719 - loss: 2.5537
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1628 - loss: 2.5609 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1651 - loss: 2.5595
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[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1670 - loss: 2.5490
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1677 - loss: 2.5463
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1682 - loss: 2.5442
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1685 - loss: 2.5431 - val_accuracy: 0.2302 - val_loss: 2.1835
Epoch 10/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1672 - loss: 2.4901 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1747 - loss: 2.4856
[1m119/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1767 - loss: 2.4835
[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1774 - loss: 2.4819
[1m199/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1777 - loss: 2.4815
[1m239/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1779 - loss: 2.4812
[1m277/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1781 - loss: 2.4806
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1781 - loss: 2.4803 - val_accuracy: 0.2387 - val_loss: 2.1483
Epoch 11/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1796 - loss: 2.4232 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1789 - loss: 2.4300
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1814 - loss: 2.4291 - val_accuracy: 0.2461 - val_loss: 2.1120
Epoch 12/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1875 - loss: 2.3437
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1897 - loss: 2.4064 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1884 - loss: 2.4072
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1873 - loss: 2.4067
[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1877 - loss: 2.4038
[1m200/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1882 - loss: 2.4011
[1m241/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1887 - loss: 2.3989
[1m279/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1891 - loss: 2.3970
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1892 - loss: 2.3963 - val_accuracy: 0.2509 - val_loss: 2.0687
Epoch 13/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1406 - loss: 2.4490
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1940 - loss: 2.3733 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1946 - loss: 2.3651
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1952 - loss: 2.3609
[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1949 - loss: 2.3584
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1948 - loss: 2.3566
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1948 - loss: 2.3546
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1947 - loss: 2.3530
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1947 - loss: 2.3524 - val_accuracy: 0.2505 - val_loss: 2.0409
Epoch 14/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.1719 - loss: 2.5125
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2083 - loss: 2.3157 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2046 - loss: 2.3133
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2041 - loss: 2.3111
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2036 - loss: 2.3097
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2033 - loss: 2.3094
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2028 - loss: 2.3100
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2025 - loss: 2.3101
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2023 - loss: 2.3100 - val_accuracy: 0.2703 - val_loss: 1.9970
Epoch 15/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2031 - loss: 2.3497
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2108 - loss: 2.2746 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2108 - loss: 2.2782
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2107 - loss: 2.2802
[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2104 - loss: 2.2798
[1m201/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2109 - loss: 2.2778
[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2113 - loss: 2.2766
[1m275/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2115 - loss: 2.2755
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2115 - loss: 2.2751 - val_accuracy: 0.2688 - val_loss: 1.9901
Epoch 16/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1406 - loss: 2.4195
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2238 - loss: 2.2586 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2252 - loss: 2.2437
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2240 - loss: 2.2384
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2226 - loss: 2.2367
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2214 - loss: 2.2362
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2208 - loss: 2.2351
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2202 - loss: 2.2341
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2198 - loss: 2.2335 - val_accuracy: 0.2727 - val_loss: 1.9404
Epoch 17/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1875 - loss: 2.3190
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2173 - loss: 2.2242 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2176 - loss: 2.2235
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2181 - loss: 2.2182
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Epoch 18/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2222 - loss: 2.2307 
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Epoch 19/110

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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2351 - loss: 2.1436
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2350 - loss: 2.1446
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2349 - loss: 2.1452 - val_accuracy: 0.2809 - val_loss: 1.8769
Epoch 20/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2656 - loss: 2.2995
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2422 - loss: 2.1346 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2378 - loss: 2.1377
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[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2348 - loss: 2.1382
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2342 - loss: 2.1382
[1m222/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2338 - loss: 2.1381
[1m263/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2336 - loss: 2.1381
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2335 - loss: 2.1383 - val_accuracy: 0.2794 - val_loss: 1.8722
Epoch 21/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2500 - loss: 2.1866
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2543 - loss: 2.1613 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2511 - loss: 2.1443
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2492 - loss: 2.1372
[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2473 - loss: 2.1340
[1m198/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2456 - loss: 2.1320
[1m239/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2444 - loss: 2.1301
[1m280/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2438 - loss: 2.1284
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2437 - loss: 2.1278 - val_accuracy: 0.3204 - val_loss: 1.8467
Epoch 22/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.1719 - loss: 2.0727
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2522 - loss: 2.0930 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2512 - loss: 2.0961
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[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2496 - loss: 2.0942
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2495 - loss: 2.0942 - val_accuracy: 0.2997 - val_loss: 1.8281
Epoch 23/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2188 - loss: 2.2659
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2511 - loss: 2.1310 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2535 - loss: 2.1161
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2545 - loss: 2.0983
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2543 - loss: 2.0948
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2542 - loss: 2.0925
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2540 - loss: 2.0900 - val_accuracy: 0.3097 - val_loss: 1.8152
Epoch 24/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3125 - loss: 2.1492
[1m 34/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2870 - loss: 2.0327 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2733 - loss: 2.0398
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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2658 - loss: 2.0467
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2646 - loss: 2.0479
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2640 - loss: 2.0490
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2634 - loss: 2.0501
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2629 - loss: 2.0510 - val_accuracy: 0.3132 - val_loss: 1.8006
Epoch 25/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2031 - loss: 2.1067
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2446 - loss: 2.0748 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2494 - loss: 2.0631
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2537 - loss: 2.0549
[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2559 - loss: 2.0517
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2567 - loss: 2.0510
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2575 - loss: 2.0505
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2583 - loss: 2.0498
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2586 - loss: 2.0495 - val_accuracy: 0.3241 - val_loss: 1.7940
Epoch 26/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2656 - loss: 2.1038
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2613 - loss: 2.0659 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2652 - loss: 2.0544
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2665 - loss: 2.0479
[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2672 - loss: 2.0446
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2677 - loss: 2.0427
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2680 - loss: 2.0411
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2679 - loss: 2.0400
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2679 - loss: 2.0396 - val_accuracy: 0.3219 - val_loss: 1.7866
Epoch 27/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1719 - loss: 1.9639
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2457 - loss: 2.0491 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2456 - loss: 2.0524
[1m106/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2488 - loss: 2.0511
[1m146/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2526 - loss: 2.0472
[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2553 - loss: 2.0438
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2573 - loss: 2.0409
[1m258/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2587 - loss: 2.0392
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2598 - loss: 2.0374 - val_accuracy: 0.3314 - val_loss: 1.7753
Epoch 28/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2750 - loss: 2.0280 
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[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2783 - loss: 2.0149
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[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2753 - loss: 2.0133
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Epoch 29/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2706 - loss: 2.0333 
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Epoch 30/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2678 - loss: 1.9986 
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2761 - loss: 1.9929
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2776 - loss: 1.9915
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2774 - loss: 1.9916
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2774 - loss: 1.9918 - val_accuracy: 0.3323 - val_loss: 1.7489
Epoch 31/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 25ms/step - accuracy: 0.2812 - loss: 2.0647
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2753 - loss: 2.0281 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2738 - loss: 2.0175
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2738 - loss: 2.0110
[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2736 - loss: 2.0081
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2740 - loss: 2.0050
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2746 - loss: 2.0021
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2754 - loss: 2.0000
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2757 - loss: 1.9989 - val_accuracy: 0.3438 - val_loss: 1.7448
Epoch 32/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2500 - loss: 2.0056
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2799 - loss: 1.9881 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2840 - loss: 1.9814
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[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2897 - loss: 1.9733
[1m198/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2904 - loss: 1.9732
[1m237/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2912 - loss: 1.9727
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2917 - loss: 1.9721
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2919 - loss: 1.9716 - val_accuracy: 0.3343 - val_loss: 1.7347
Epoch 33/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2941 - loss: 1.9900 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2938 - loss: 1.9846
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Epoch 34/110

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Epoch 35/110

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Epoch 36/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2952 - loss: 1.9345 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2920 - loss: 1.9402
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[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2933 - loss: 1.9371
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2938 - loss: 1.9355 - val_accuracy: 0.3456 - val_loss: 1.7104
Epoch 37/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2884 - loss: 1.9209 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2919 - loss: 1.9212
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[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2937 - loss: 1.9259
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2938 - loss: 1.9263
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2940 - loss: 1.9267
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Epoch 38/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2999 - loss: 1.8991 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3059 - loss: 1.8968
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3056 - loss: 1.9010
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3059 - loss: 1.9022
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Epoch 39/110

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Epoch 40/110

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[1m 32/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3089 - loss: 1.8400 
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Epoch 41/110

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Epoch 42/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3117 - loss: 1.9300 
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[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3097 - loss: 1.9065
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3096 - loss: 1.9040
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3096 - loss: 1.9027
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3096 - loss: 1.9019 - val_accuracy: 0.3404 - val_loss: 1.6800
Epoch 43/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3242 - loss: 1.8507 
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[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3235 - loss: 1.8531
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3226 - loss: 1.8555
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3220 - loss: 1.8579
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Epoch 44/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3355 - loss: 1.8604 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3294 - loss: 1.8637
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Epoch 45/110

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Epoch 46/110

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Epoch 47/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3078 - loss: 1.8911 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3113 - loss: 1.8830
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[1m241/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3132 - loss: 1.8770
[1m278/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3139 - loss: 1.8754
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3141 - loss: 1.8748 - val_accuracy: 0.3500 - val_loss: 1.6602
Epoch 48/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3096 - loss: 1.8618 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3107 - loss: 1.8564
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[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3153 - loss: 1.8507
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3158 - loss: 1.8505
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3161 - loss: 1.8502 - val_accuracy: 0.3610 - val_loss: 1.6606
Epoch 49/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3460 - loss: 1.8063 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3429 - loss: 1.8046
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[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3352 - loss: 1.8232
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Epoch 50/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3280 - loss: 1.8700 
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Epoch 51/110

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Epoch 52/110

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Epoch 53/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3355 - loss: 1.7815 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3380 - loss: 1.7896
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[1m243/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3333 - loss: 1.8054
[1m283/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3328 - loss: 1.8070
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Epoch 54/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3263 - loss: 1.8368 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3276 - loss: 1.8362
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3310 - loss: 1.8286
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3313 - loss: 1.8272
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Epoch 55/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3214 - loss: 1.8130 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3238 - loss: 1.8123
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Epoch 56/110

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Epoch 57/110

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Epoch 58/110

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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3325 - loss: 1.8034
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3326 - loss: 1.8033 - val_accuracy: 0.3539 - val_loss: 1.6353
Epoch 59/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3306 - loss: 1.8562 
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[1m255/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3318 - loss: 1.8208
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Epoch 60/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3787 - loss: 1.7427 
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Epoch 61/110

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Epoch 62/110

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Epoch 63/110

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Epoch 64/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3394 - loss: 1.8026 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3408 - loss: 1.7811
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3372 - loss: 1.7812
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3372 - loss: 1.7813
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Epoch 65/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3329 - loss: 1.7847 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3400 - loss: 1.7784
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[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3437 - loss: 1.7775
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3436 - loss: 1.7794
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Epoch 66/110

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Epoch 67/110

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Epoch 68/110

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Epoch 69/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3546 - loss: 1.7138 
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[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3482 - loss: 1.7416
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3473 - loss: 1.7459 - val_accuracy: 0.3580 - val_loss: 1.6136
Epoch 70/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3326 - loss: 1.7383 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3376 - loss: 1.7450
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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3384 - loss: 1.7605
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3384 - loss: 1.7617
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Epoch 71/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3443 - loss: 1.8271 
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3452 - loss: 1.7755
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Epoch 72/110

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Epoch 73/110

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[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3442 - loss: 1.7484
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Epoch 74/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3610 - loss: 1.7044 
[1m 69/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3586 - loss: 1.7296
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[1m217/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3513 - loss: 1.7515
[1m256/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3509 - loss: 1.7520
[1m291/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3506 - loss: 1.7523
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3506 - loss: 1.7523 - val_accuracy: 0.3606 - val_loss: 1.6126
Epoch 75/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3613 - loss: 1.7296 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3530 - loss: 1.7444
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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3507 - loss: 1.7465
[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3508 - loss: 1.7458
[1m222/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3508 - loss: 1.7455
[1m259/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3508 - loss: 1.7448
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3507 - loss: 1.7446 - val_accuracy: 0.3671 - val_loss: 1.6102
Epoch 76/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3527 - loss: 1.7173 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3468 - loss: 1.7312
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3451 - loss: 1.7406
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Epoch 77/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3558 - loss: 1.7218 
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Epoch 78/110

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Epoch 79/110

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Epoch 80/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3629 - loss: 1.7445 
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3566 - loss: 1.7304
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3558 - loss: 1.7292 - val_accuracy: 0.3673 - val_loss: 1.5990
Epoch 81/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3580 - loss: 1.7351 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3573 - loss: 1.7378
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3540 - loss: 1.7333
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3532 - loss: 1.7337
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Epoch 82/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3490 - loss: 1.7522 
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3562 - loss: 1.7374 - val_accuracy: 0.3780 - val_loss: 1.6061
Epoch 83/110

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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3562 - loss: 1.7085
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Epoch 84/110

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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3467 - loss: 1.7365
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3479 - loss: 1.7337 - val_accuracy: 0.3708 - val_loss: 1.6000
Epoch 85/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3438 - loss: 1.5730
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[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3600 - loss: 1.6991
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[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3614 - loss: 1.7088
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[1m64/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 809us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
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Saved model to disk.

Accuracy capturado en la ejecución 15: 35.51 [%]
F1-score capturado en la ejecución 15: 33.59 [%]

=== EJECUCIÓN 16 ===

--- TRAIN (ejecución 16) ---

--- TEST (ejecución 16) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18676, 6, 250)

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[1m 72/169[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 708us/step
[1m141/169[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 718us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 37.84 [%]
Global F1 score (validation) = 36.71 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[2.04534858e-01 1.99469656e-01 2.07687393e-01 ... 6.37833318e-06
  1.61175981e-01 1.44955833e-02]
 [2.13023394e-01 2.05340251e-01 2.16436014e-01 ... 8.28925840e-06
  1.37658805e-01 8.37516133e-03]
 [2.06345215e-01 2.16151267e-01 2.09492311e-01 ... 2.53894414e-05
  1.38904497e-01 1.03260111e-02]
 ...
 [2.09561989e-01 1.94992647e-01 2.13576138e-01 ... 4.31934968e-05
  1.43383697e-01 2.32943706e-02]
 [1.55068681e-01 1.92019701e-01 1.76451311e-01 ... 8.50030337e-04
  1.69496715e-01 8.25449452e-03]
 [2.16060560e-02 3.12481001e-02 2.38799993e-02 ... 1.14819035e-02
  3.04845925e-02 2.60130479e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 44.44 [%]
Global accuracy score (test) = 36.21 [%]
Global F1 score (train) = 43.36 [%]
Global F1 score (test) = 34.82 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.25      0.18      0.21       184
 CAMINAR CON MÓVIL O LIBRO       0.22      0.22      0.22       184
       CAMINAR USUAL SPEED       0.16      0.11      0.13       184
            CAMINAR ZIGZAG       0.19      0.32      0.24       184
          DE PIE BARRIENDO       0.27      0.72      0.39       184
   DE PIE DOBLANDO TOALLAS       0.15      0.02      0.03       184
    DE PIE MOVIENDO LIBROS       0.30      0.40      0.34       184
          DE PIE USANDO PC       0.44      0.60      0.51       184
        FASE REPOSO CON K5       0.73      0.82      0.77       184
INCREMENTAL CICLOERGOMETRO       0.91      0.59      0.71       184
           SENTADO LEYENDO       0.30      0.35      0.32       184
         SENTADO USANDO PC       0.22      0.09      0.13       184
      SENTADO VIENDO LA TV       0.27      0.22      0.24       184
   SUBIR Y BAJAR ESCALERAS       0.40      0.15      0.22       184
                    TROTAR       0.84      0.69      0.76       161

                  accuracy                           0.36      2737
                 macro avg       0.38      0.36      0.35      2737
              weighted avg       0.37      0.36      0.34      2737

2025-10-28 14:18:59.555923: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 14:18:59.567357: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1761657539.580775 2336071 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761657539.585032 2336071 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761657539.594874 2336071 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657539.594896 2336071 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657539.594900 2336071 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657539.594902 2336071 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 14:18:59.598153: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1761657541.991205 2336071 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13733 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/110
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761657543.707064 2336196 service.cc:152] XLA service 0x7c9998002ee0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761657543.707105 2336196 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 14:19:03.740749: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
I0000 00:00:1761657543.905447 2336196 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761657546.189616 2336196 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0613 - loss: 3.6210
[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0639 - loss: 3.5991
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[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0681 - loss: 3.5624
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.0699 - loss: 3.5478
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step - accuracy: 0.0709 - loss: 3.5392
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 16ms/step - accuracy: 0.0710 - loss: 3.5389 - val_accuracy: 0.1534 - val_loss: 2.4847
Epoch 2/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1170 - loss: 3.2348 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1119 - loss: 3.2366
[1m107/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1104 - loss: 3.2330
[1m146/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1095 - loss: 3.2265
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[1m214/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1086 - loss: 3.2160
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Epoch 3/110

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Epoch 4/110

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Epoch 5/110

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Epoch 6/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1453 - loss: 2.8147 
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[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1401 - loss: 2.7781
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1401 - loss: 2.7725 - val_accuracy: 0.2037 - val_loss: 2.2736
Epoch 7/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1432 - loss: 2.7060 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1455 - loss: 2.7004
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[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1490 - loss: 2.6927
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1498 - loss: 2.6876
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1501 - loss: 2.6848
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Epoch 8/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1464 - loss: 2.6520 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1513 - loss: 2.6350
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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1553 - loss: 2.6220
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Epoch 9/110

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Epoch 10/110

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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1781 - loss: 2.4906
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Epoch 11/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1875 - loss: 2.4007
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1949 - loss: 2.4257 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1928 - loss: 2.4304
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[1m215/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1876 - loss: 2.4361
[1m249/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1869 - loss: 2.4359
[1m289/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1866 - loss: 2.4349
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1865 - loss: 2.4348 - val_accuracy: 0.2383 - val_loss: 2.0836
Epoch 12/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2188 - loss: 2.3536
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1817 - loss: 2.3980 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1877 - loss: 2.3911
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1902 - loss: 2.3891
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1915 - loss: 2.3868
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1923 - loss: 2.3851
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1927 - loss: 2.3840
[1m259/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1928 - loss: 2.3836
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1928 - loss: 2.3831 - val_accuracy: 0.2448 - val_loss: 2.0527
Epoch 13/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.1719 - loss: 2.3565
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1974 - loss: 2.3050 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1980 - loss: 2.3208
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1981 - loss: 2.3286
[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1975 - loss: 2.3332
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1971 - loss: 2.3355
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1970 - loss: 2.3368
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1970 - loss: 2.3374
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Epoch 14/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2188 - loss: 2.2624
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1930 - loss: 2.3273 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1928 - loss: 2.3215
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[1m147/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1940 - loss: 2.3192
[1m183/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1952 - loss: 2.3170
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1962 - loss: 2.3153
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1978 - loss: 2.3130 - val_accuracy: 0.2668 - val_loss: 2.0036
Epoch 15/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2031 - loss: 2.3328
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1912 - loss: 2.3025 
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[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2005 - loss: 2.2923
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Epoch 16/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1250 - loss: 2.4001
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2076 - loss: 2.2577 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2100 - loss: 2.2508
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[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2089 - loss: 2.2479
[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2086 - loss: 2.2463
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2085 - loss: 2.2457
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2086 - loss: 2.2450
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2085 - loss: 2.2446 - val_accuracy: 0.2744 - val_loss: 1.9635
Epoch 17/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1562 - loss: 2.2892
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2180 - loss: 2.2123 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2226 - loss: 2.2081
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2229 - loss: 2.2096
[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2226 - loss: 2.2106
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2224 - loss: 2.2116
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2225 - loss: 2.2124
[1m258/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2225 - loss: 2.2133
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2224 - loss: 2.2140 - val_accuracy: 0.2747 - val_loss: 1.9414
Epoch 18/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1875 - loss: 2.4067
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2242 - loss: 2.2269 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2248 - loss: 2.2189
[1m109/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2250 - loss: 2.2142
[1m147/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2247 - loss: 2.2097
[1m183/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2242 - loss: 2.2072
[1m222/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2238 - loss: 2.2053
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2236 - loss: 2.2032
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2235 - loss: 2.2016 - val_accuracy: 0.2827 - val_loss: 1.9232
Epoch 19/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2344 - loss: 2.1559
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2396 - loss: 2.1434 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2349 - loss: 2.1612
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2321 - loss: 2.1668
[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2307 - loss: 2.1695
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2299 - loss: 2.1705
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2293 - loss: 2.1707
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2287 - loss: 2.1707
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Epoch 20/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2188 - loss: 2.0421
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2322 - loss: 2.1640 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2342 - loss: 2.1515
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[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2345 - loss: 2.1460
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[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2349 - loss: 2.1423
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2347 - loss: 2.1411 - val_accuracy: 0.2969 - val_loss: 1.8914
Epoch 21/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2500 - loss: 2.1281
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2284 - loss: 2.1612 
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[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2329 - loss: 2.1423
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2331 - loss: 2.1405
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2332 - loss: 2.1392
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2334 - loss: 2.1381
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2335 - loss: 2.1372 - val_accuracy: 0.3110 - val_loss: 1.8660
Epoch 22/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2188 - loss: 2.1456
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2317 - loss: 2.0986 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2327 - loss: 2.1167
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[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2343 - loss: 2.1180
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2348 - loss: 2.1177
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2350 - loss: 2.1171
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2352 - loss: 2.1166
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2353 - loss: 2.1162 - val_accuracy: 0.3049 - val_loss: 1.8520
Epoch 23/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3125 - loss: 2.1997
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2459 - loss: 2.1208 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2439 - loss: 2.1144
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2439 - loss: 2.1117
[1m147/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2441 - loss: 2.1087
[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2439 - loss: 2.1062
[1m220/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2437 - loss: 2.1042
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2438 - loss: 2.1023
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2438 - loss: 2.1011 - val_accuracy: 0.3064 - val_loss: 1.8765
Epoch 24/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.2188 - loss: 2.2653
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2501 - loss: 2.0928 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2492 - loss: 2.0870
[1m120/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2485 - loss: 2.0863
[1m161/292[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2484 - loss: 2.0862
[1m199/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2480 - loss: 2.0858
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2479 - loss: 2.0851
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2478 - loss: 2.0847
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2477 - loss: 2.0846 - val_accuracy: 0.3164 - val_loss: 1.8459
Epoch 25/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.1406 - loss: 2.1314
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2237 - loss: 2.0863 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2354 - loss: 2.0776
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[1m146/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2414 - loss: 2.0745
[1m182/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2430 - loss: 2.0730
[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2444 - loss: 2.0716
[1m259/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2453 - loss: 2.0707
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2459 - loss: 2.0703 - val_accuracy: 0.3143 - val_loss: 1.8380
Epoch 26/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2188 - loss: 2.2128
[1m 42/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2434 - loss: 2.0890 
[1m 82/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2467 - loss: 2.0731
[1m122/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2470 - loss: 2.0694
[1m162/292[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2482 - loss: 2.0666
[1m201/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2491 - loss: 2.0645
[1m241/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2498 - loss: 2.0633
[1m280/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2503 - loss: 2.0622
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2505 - loss: 2.0617 - val_accuracy: 0.3188 - val_loss: 1.8234
Epoch 27/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1719 - loss: 2.1361
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2304 - loss: 2.0795 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2434 - loss: 2.0611
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2475 - loss: 2.0545
[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2503 - loss: 2.0496
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2523 - loss: 2.0463
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2533 - loss: 2.0450
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2537 - loss: 2.0446
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2540 - loss: 2.0442 - val_accuracy: 0.3323 - val_loss: 1.8144
Epoch 28/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2969 - loss: 1.9575
[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2520 - loss: 2.0503 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2562 - loss: 2.0511
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2575 - loss: 2.0504
[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2585 - loss: 2.0486
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2593 - loss: 2.0473
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2597 - loss: 2.0461
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2602 - loss: 2.0444
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2604 - loss: 2.0436 - val_accuracy: 0.3125 - val_loss: 1.8075
Epoch 29/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2812 - loss: 1.9009
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2611 - loss: 2.0227 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2685 - loss: 2.0137
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2715 - loss: 2.0093
[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2731 - loss: 2.0069
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2731 - loss: 2.0074
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2725 - loss: 2.0084
[1m275/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2719 - loss: 2.0092
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2717 - loss: 2.0095 - val_accuracy: 0.3256 - val_loss: 1.8020
Epoch 30/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1875 - loss: 2.3041
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2562 - loss: 2.0471 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2600 - loss: 2.0266
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2608 - loss: 2.0235
[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2612 - loss: 2.0219
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2615 - loss: 2.0203
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2615 - loss: 2.0193
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2616 - loss: 2.0186
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2617 - loss: 2.0184 - val_accuracy: 0.3236 - val_loss: 1.7900
Epoch 31/110

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Epoch 32/110

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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2703 - loss: 1.9901
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Epoch 33/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2744 - loss: 2.0088 
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[1m220/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2778 - loss: 1.9903
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2781 - loss: 1.9871 - val_accuracy: 0.3338 - val_loss: 1.7613
Epoch 34/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2840 - loss: 1.9470 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2803 - loss: 1.9633
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[1m183/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2793 - loss: 1.9685
[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2794 - loss: 1.9691
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2793 - loss: 1.9708 - val_accuracy: 0.3365 - val_loss: 1.7622
Epoch 35/110

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[1m 34/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3007 - loss: 1.9479 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2939 - loss: 1.9546
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[1m143/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2909 - loss: 1.9590
[1m183/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2900 - loss: 1.9604
[1m220/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2889 - loss: 1.9616
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Epoch 36/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2896 - loss: 1.9838 
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Epoch 37/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2958 - loss: 1.9044 
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Epoch 38/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2704 - loss: 1.9521 
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2861 - loss: 1.9547
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2866 - loss: 1.9536 - val_accuracy: 0.3375 - val_loss: 1.7382
Epoch 39/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3438 - loss: 1.7741
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3191 - loss: 1.9172 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3112 - loss: 1.9231
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[1m220/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3065 - loss: 1.9246
[1m259/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3058 - loss: 1.9249
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3050 - loss: 1.9254 - val_accuracy: 0.3510 - val_loss: 1.7219
Epoch 40/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3163 - loss: 1.8985 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3093 - loss: 1.9178
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[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3051 - loss: 1.9247
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3047 - loss: 1.9243
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3043 - loss: 1.9243
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3034 - loss: 1.9248
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3030 - loss: 1.9247 - val_accuracy: 0.3500 - val_loss: 1.7274
Epoch 41/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 25ms/step - accuracy: 0.3594 - loss: 1.6447
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2999 - loss: 1.9381 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3001 - loss: 1.9296
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[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2994 - loss: 1.9241
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[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2995 - loss: 1.9231
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2996 - loss: 1.9222
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Epoch 42/110

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Epoch 43/110

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Epoch 44/110

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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3107 - loss: 1.8800
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Epoch 45/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3163 - loss: 1.9007 
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[1m289/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3110 - loss: 1.9015
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Epoch 46/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3092 - loss: 1.9026 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3083 - loss: 1.9007
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Epoch 47/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3094 - loss: 1.8941 
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Epoch 48/110

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Epoch 49/110

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Epoch 50/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3204 - loss: 1.8646 
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[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3213 - loss: 1.8533
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3200 - loss: 1.8545
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3189 - loss: 1.8558
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3182 - loss: 1.8565 - val_accuracy: 0.3562 - val_loss: 1.6639
Epoch 51/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3358 - loss: 1.8204 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3305 - loss: 1.8358
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3224 - loss: 1.8549
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3218 - loss: 1.8562
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Epoch 52/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3062 - loss: 1.8889 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3141 - loss: 1.8786
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Epoch 53/110

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Epoch 54/110

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Epoch 55/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3137 - loss: 1.8434 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3123 - loss: 1.8545
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3175 - loss: 1.8511
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3193 - loss: 1.8477 - val_accuracy: 0.3525 - val_loss: 1.6453
Epoch 56/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3380 - loss: 1.8325 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3380 - loss: 1.8256
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[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3366 - loss: 1.8197
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3362 - loss: 1.8192
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3356 - loss: 1.8196
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3352 - loss: 1.8202 - val_accuracy: 0.3654 - val_loss: 1.6448
Epoch 57/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3213 - loss: 1.7995 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3247 - loss: 1.8041
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3256 - loss: 1.8141
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Epoch 58/110

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Epoch 59/110

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Epoch 60/110

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Epoch 61/110

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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3288 - loss: 1.8151
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3295 - loss: 1.8131 - val_accuracy: 0.3650 - val_loss: 1.6253
Epoch 62/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3436 - loss: 1.7826 
[1m 82/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3440 - loss: 1.7862
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[1m241/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3402 - loss: 1.7925
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Epoch 63/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3207 - loss: 1.8116 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3268 - loss: 1.8071
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Epoch 64/110

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Epoch 65/110

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Epoch 66/110

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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3409 - loss: 1.7830
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3413 - loss: 1.7831
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Epoch 67/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3473 - loss: 1.7606 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3478 - loss: 1.7734
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[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3434 - loss: 1.7866
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Epoch 68/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3405 - loss: 1.7986 
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Epoch 69/110

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Epoch 70/110

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Epoch 71/110

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Epoch 72/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3385 - loss: 1.7888 
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[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3436 - loss: 1.7732
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3438 - loss: 1.7727 - val_accuracy: 0.3680 - val_loss: 1.6055
Epoch 73/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3140 - loss: 1.8198 
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[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3345 - loss: 1.7789
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Epoch 74/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3370 - loss: 1.7713 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3351 - loss: 1.7643
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3391 - loss: 1.7574
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3396 - loss: 1.7578
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Epoch 75/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3344 - loss: 1.7626 
[1m 69/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3385 - loss: 1.7587
[1m107/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3408 - loss: 1.7608
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[1m182/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3427 - loss: 1.7636
[1m219/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3428 - loss: 1.7650
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Epoch 76/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3906 - loss: 1.8115
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3567 - loss: 1.7564 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3537 - loss: 1.7611
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3491 - loss: 1.7599
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3484 - loss: 1.7599
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3474 - loss: 1.7606
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3465 - loss: 1.7608
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3462 - loss: 1.7607 - val_accuracy: 0.3680 - val_loss: 1.6124
Epoch 77/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.4531 - loss: 1.5016
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3854 - loss: 1.7017 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3728 - loss: 1.7176
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3665 - loss: 1.7240
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3625 - loss: 1.7294
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3602 - loss: 1.7344
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3588 - loss: 1.7372
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3575 - loss: 1.7400
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3568 - loss: 1.7413 - val_accuracy: 0.3652 - val_loss: 1.6011
Epoch 78/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3438 - loss: 1.8210
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3248 - loss: 1.7559 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3290 - loss: 1.7621
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3325 - loss: 1.7642
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3347 - loss: 1.7657
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3366 - loss: 1.7656
[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3379 - loss: 1.7650
[1m254/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3390 - loss: 1.7645
[1m290/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3400 - loss: 1.7638
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3400 - loss: 1.7637 - val_accuracy: 0.3759 - val_loss: 1.6022
Epoch 79/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3125 - loss: 1.7128
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3529 - loss: 1.7375 
[1m 82/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3558 - loss: 1.7444
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3559 - loss: 1.7421
[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3549 - loss: 1.7424
[1m197/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3539 - loss: 1.7431
[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3531 - loss: 1.7439
[1m276/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3525 - loss: 1.7451
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Epoch 80/110

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Epoch 81/110

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Epoch 82/110

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Epoch 83/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3513 - loss: 1.7086 
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Epoch 84/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3470 - loss: 1.7467 
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Epoch 85/110

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[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3417 - loss: 1.7658
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3425 - loss: 1.7616
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3439 - loss: 1.7558 - val_accuracy: 0.3765 - val_loss: 1.5951
Epoch 86/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.3594 - loss: 1.7426
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[1m146/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3464 - loss: 1.7415
[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3483 - loss: 1.7382
[1m219/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3495 - loss: 1.7359
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3505 - loss: 1.7338 - val_accuracy: 0.3880 - val_loss: 1.5945
Epoch 87/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3438 - loss: 1.9002
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[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3469 - loss: 1.7415
[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3463 - loss: 1.7406
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3458 - loss: 1.7412
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3457 - loss: 1.7410
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3458 - loss: 1.7404 - val_accuracy: 0.3824 - val_loss: 1.5983
Epoch 88/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3906 - loss: 1.7192
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3372 - loss: 1.7514 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3365 - loss: 1.7499
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[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3395 - loss: 1.7416
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3403 - loss: 1.7406
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3415 - loss: 1.7388
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3432 - loss: 1.7364 - val_accuracy: 0.3815 - val_loss: 1.6020

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:13[0m 864ms/step
[1m66/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 773us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 9ms/step
Saved model to disk.

Accuracy capturado en la ejecución 16: 36.21 [%]
F1-score capturado en la ejecución 16: 34.82 [%]

=== EJECUCIÓN 17 ===

--- TRAIN (ejecución 17) ---

--- TEST (ejecución 17) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18676, 6, 250)

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[1m 66/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 775us/step
[1m139/169[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 729us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 38.15 [%]
Global F1 score (validation) = 38.11 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[2.0890598e-01 1.9924930e-01 2.0623265e-01 ... 3.9268630e-06
  1.5018533e-01 9.9780858e-03]
 [2.0085211e-01 2.0608191e-01 2.0949689e-01 ... 7.2970561e-06
  1.4798540e-01 8.1580542e-03]
 [2.0675960e-01 1.7573802e-01 1.9433595e-01 ... 6.3960174e-06
  1.6839279e-01 4.8967808e-02]
 ...
 [1.8258671e-01 2.1716172e-01 2.0246813e-01 ... 1.9657084e-04
  1.6086851e-01 1.5591040e-02]
 [1.8127403e-01 2.1540000e-01 2.0170961e-01 ... 7.5974269e-05
  1.5750709e-01 1.2883979e-02]
 [2.6300106e-02 4.0342506e-02 3.1948429e-02 ... 2.3800341e-02
  4.5077223e-02 3.7247343e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 44.37 [%]
Global accuracy score (test) = 35.0 [%]
Global F1 score (train) = 43.21 [%]
Global F1 score (test) = 34.26 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.22      0.45      0.30       184
 CAMINAR CON MÓVIL O LIBRO       0.20      0.24      0.22       184
       CAMINAR USUAL SPEED       0.00      0.00      0.00       184
            CAMINAR ZIGZAG       0.17      0.12      0.14       184
          DE PIE BARRIENDO       0.24      0.45      0.31       184
   DE PIE DOBLANDO TOALLAS       0.24      0.22      0.23       184
    DE PIE MOVIENDO LIBROS       0.25      0.24      0.25       184
          DE PIE USANDO PC       0.42      0.53      0.47       184
        FASE REPOSO CON K5       0.65      0.76      0.70       184
INCREMENTAL CICLOERGOMETRO       0.95      0.60      0.74       184
           SENTADO LEYENDO       0.32      0.43      0.37       184
         SENTADO USANDO PC       0.16      0.07      0.10       184
      SENTADO VIENDO LA TV       0.27      0.21      0.24       184
   SUBIR Y BAJAR ESCALERAS       0.34      0.23      0.28       184
                    TROTAR       0.92      0.73      0.81       161

                  accuracy                           0.35      2737
                 macro avg       0.36      0.35      0.34      2737
              weighted avg       0.35      0.35      0.34      2737

2025-10-28 14:20:11.972937: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 14:20:11.984378: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1761657611.997787 2345327 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761657612.001947 2345327 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761657612.012326 2345327 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657612.012350 2345327 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657612.012352 2345327 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657612.012354 2345327 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 14:20:12.015543: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1761657614.368691 2345327 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13733 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/110
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761657616.072964 2345428 service.cc:152] XLA service 0x716b7000c990 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761657616.073036 2345428 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 14:20:16.113625: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
I0000 00:00:1761657616.277537 2345428 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761657618.541590 2345428 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16:42[0m 3s/step - accuracy: 0.1562 - loss: 3.3112
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[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0720 - loss: 3.5821
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0739 - loss: 3.5612
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0754 - loss: 3.5429
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.0767 - loss: 3.5265
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - accuracy: 0.0772 - loss: 3.5194
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 16ms/step - accuracy: 0.0772 - loss: 3.5190 - val_accuracy: 0.1602 - val_loss: 2.5062
Epoch 2/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1105 - loss: 3.2995 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1085 - loss: 3.2878
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1082 - loss: 3.2772
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1082 - loss: 3.2682
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1085 - loss: 3.2599
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1089 - loss: 3.2523
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1092 - loss: 3.2454
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Epoch 3/110

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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1139 - loss: 3.0887
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Epoch 4/110

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Epoch 5/110

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[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1299 - loss: 2.8840
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1311 - loss: 2.8747 - val_accuracy: 0.1928 - val_loss: 2.3331
Epoch 6/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.0938 - loss: 3.0473
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1284 - loss: 2.8307 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1300 - loss: 2.8273
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[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1358 - loss: 2.8075
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1370 - loss: 2.8017
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1381 - loss: 2.7957
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1389 - loss: 2.7913
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1392 - loss: 2.7888 - val_accuracy: 0.1954 - val_loss: 2.3102
Epoch 7/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.1562 - loss: 2.8280
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1348 - loss: 2.7975 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1350 - loss: 2.7809
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[1m147/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1384 - loss: 2.7507
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1398 - loss: 2.7416
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1408 - loss: 2.7347
[1m262/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1417 - loss: 2.7286
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1422 - loss: 2.7247 - val_accuracy: 0.2019 - val_loss: 2.2738
Epoch 8/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1406 - loss: 2.7021
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1450 - loss: 2.6991 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1480 - loss: 2.6713
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1506 - loss: 2.6596
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1521 - loss: 2.6535
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1535 - loss: 2.6456
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1537 - loss: 2.6432
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Epoch 9/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1649 - loss: 2.5744 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1631 - loss: 2.5751
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1635 - loss: 2.5675
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Epoch 10/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1250 - loss: 2.6814
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[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1696 - loss: 2.5205
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1719 - loss: 2.5127
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1723 - loss: 2.5103 - val_accuracy: 0.2309 - val_loss: 2.1581
Epoch 11/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1719 - loss: 2.4883
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1853 - loss: 2.4602 
[1m 82/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1792 - loss: 2.4716
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[1m164/292[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1759 - loss: 2.4737
[1m204/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1757 - loss: 2.4715
[1m243/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1757 - loss: 2.4695
[1m282/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1760 - loss: 2.4673
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1760 - loss: 2.4667 - val_accuracy: 0.2440 - val_loss: 2.1071
Epoch 12/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1406 - loss: 2.4613
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1831 - loss: 2.4031 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1834 - loss: 2.4100
[1m121/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1839 - loss: 2.4127
[1m162/292[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1841 - loss: 2.4145
[1m204/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1840 - loss: 2.4148
[1m246/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1838 - loss: 2.4151
[1m283/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1837 - loss: 2.4146
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1838 - loss: 2.4144 - val_accuracy: 0.2339 - val_loss: 2.0940
Epoch 13/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1406 - loss: 2.4374
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1882 - loss: 2.3604 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1931 - loss: 2.3657
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1931 - loss: 2.3696
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1931 - loss: 2.3702
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1930 - loss: 2.3701
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1927 - loss: 2.3694
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1926 - loss: 2.3682
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1924 - loss: 2.3677 - val_accuracy: 0.2538 - val_loss: 2.0454
Epoch 14/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2013 - loss: 2.3160 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2006 - loss: 2.3325
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[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2023 - loss: 2.3348
[1m199/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2026 - loss: 2.3344
[1m239/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2027 - loss: 2.3332
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2025 - loss: 2.3318 - val_accuracy: 0.2635 - val_loss: 2.0034
Epoch 15/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.2188 - loss: 2.2686
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2010 - loss: 2.3210 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2029 - loss: 2.3121
[1m120/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2051 - loss: 2.3057
[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2051 - loss: 2.3025
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2050 - loss: 2.3012
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2050 - loss: 2.2999
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2048 - loss: 2.2987 - val_accuracy: 0.2618 - val_loss: 1.9972
Epoch 16/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.0938 - loss: 2.3754
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2058 - loss: 2.2491 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2109 - loss: 2.2478
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2123 - loss: 2.2513
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2123 - loss: 2.2540
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2124 - loss: 2.2560
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2125 - loss: 2.2568
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2126 - loss: 2.2570
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2128 - loss: 2.2567 - val_accuracy: 0.2772 - val_loss: 1.9576
Epoch 17/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2031 - loss: 2.2448
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2075 - loss: 2.2576 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2070 - loss: 2.2560
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2077 - loss: 2.2513
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2091 - loss: 2.2483
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2101 - loss: 2.2458
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2108 - loss: 2.2435
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2114 - loss: 2.2418
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2117 - loss: 2.2407 - val_accuracy: 0.2738 - val_loss: 1.9448
Epoch 18/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2656 - loss: 2.1436
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2376 - loss: 2.1906 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2363 - loss: 2.1836
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2338 - loss: 2.1837
[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2321 - loss: 2.1841
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2308 - loss: 2.1850
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2295 - loss: 2.1863
[1m262/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2285 - loss: 2.1873
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2276 - loss: 2.1882 - val_accuracy: 0.2947 - val_loss: 1.9167
Epoch 19/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2500 - loss: 2.1770
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2257 - loss: 2.2204 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2263 - loss: 2.2080
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2260 - loss: 2.2027
[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2259 - loss: 2.1981
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2256 - loss: 2.1958
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2256 - loss: 2.1934
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2259 - loss: 2.1907
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Epoch 20/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2188 - loss: 1.9938
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2293 - loss: 2.1317 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2315 - loss: 2.1370
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2311 - loss: 2.1435
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2308 - loss: 2.1510
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Epoch 21/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.2188 - loss: 2.1526
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2340 - loss: 2.1591 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2362 - loss: 2.1551
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[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2383 - loss: 2.1517
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2387 - loss: 2.1508
[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2387 - loss: 2.1503
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2389 - loss: 2.1490
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2391 - loss: 2.1478 - val_accuracy: 0.3058 - val_loss: 1.8695
Epoch 22/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.2031 - loss: 2.1892
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2313 - loss: 2.1347 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2346 - loss: 2.1341
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2374 - loss: 2.1315
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2387 - loss: 2.1305
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2395 - loss: 2.1287
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2401 - loss: 2.1266
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2405 - loss: 2.1251
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2407 - loss: 2.1245 - val_accuracy: 0.3036 - val_loss: 1.8462
Epoch 23/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1875 - loss: 2.1860
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2361 - loss: 2.1051 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2322 - loss: 2.1134
[1m111/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2319 - loss: 2.1166
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2319 - loss: 2.1177
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2324 - loss: 2.1179
[1m222/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2327 - loss: 2.1181
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2332 - loss: 2.1178
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2336 - loss: 2.1177 - val_accuracy: 0.3167 - val_loss: 1.8340
Epoch 24/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2500 - loss: 2.0769
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2602 - loss: 2.0828 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2569 - loss: 2.0884
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2569 - loss: 2.0863
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2573 - loss: 2.0836
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2571 - loss: 2.0826
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2570 - loss: 2.0819
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2571 - loss: 2.0812
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2570 - loss: 2.0810 - val_accuracy: 0.3143 - val_loss: 1.8265
Epoch 25/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3906 - loss: 1.8491
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2620 - loss: 2.0474 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2499 - loss: 2.0635
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[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2471 - loss: 2.0751
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2472 - loss: 2.0769
[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2475 - loss: 2.0776
[1m276/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2480 - loss: 2.0776
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2481 - loss: 2.0775 - val_accuracy: 0.3023 - val_loss: 1.8111
Epoch 26/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3281 - loss: 1.8580
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2464 - loss: 2.0671 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2538 - loss: 2.0623
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2563 - loss: 2.0580
[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2577 - loss: 2.0560
[1m198/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2588 - loss: 2.0540
[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2594 - loss: 2.0527
[1m278/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2599 - loss: 2.0520
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2601 - loss: 2.0519 - val_accuracy: 0.3069 - val_loss: 1.8036
Epoch 27/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2812 - loss: 2.0719
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2491 - loss: 2.0738 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2484 - loss: 2.0794
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2493 - loss: 2.0777
[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2499 - loss: 2.0758
[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2505 - loss: 2.0749
[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2514 - loss: 2.0731
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2524 - loss: 2.0709
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2532 - loss: 2.0693 - val_accuracy: 0.3191 - val_loss: 1.7801
Epoch 28/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2969 - loss: 2.0400
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2566 - loss: 2.0244 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2565 - loss: 2.0312
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2563 - loss: 2.0358
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2561 - loss: 2.0369
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2566 - loss: 2.0359
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2569 - loss: 2.0354
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2574 - loss: 2.0351
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2577 - loss: 2.0349 - val_accuracy: 0.3228 - val_loss: 1.7708
Epoch 29/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3125 - loss: 1.8743
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2807 - loss: 1.9960 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2788 - loss: 2.0065
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2762 - loss: 2.0108
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2741 - loss: 2.0134
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2729 - loss: 2.0152
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2720 - loss: 2.0163
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2716 - loss: 2.0161
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2715 - loss: 2.0160 - val_accuracy: 0.3291 - val_loss: 1.7746
Epoch 30/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 25ms/step - accuracy: 0.2656 - loss: 2.0860
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2665 - loss: 2.0384 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2699 - loss: 2.0304
[1m109/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2709 - loss: 2.0247
[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2716 - loss: 2.0205
[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2723 - loss: 2.0179
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2727 - loss: 2.0164
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2731 - loss: 2.0156
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Epoch 31/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2592 - loss: 1.9942 
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Epoch 32/110

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[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2787 - loss: 1.9787
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Epoch 33/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2788 - loss: 1.9768 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2825 - loss: 1.9787
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[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2813 - loss: 1.9789
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2816 - loss: 1.9784
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2818 - loss: 1.9784 - val_accuracy: 0.3343 - val_loss: 1.7325
Epoch 34/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2812 - loss: 1.7654
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2908 - loss: 1.9887 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2818 - loss: 1.9879
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[1m145/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2771 - loss: 1.9895
[1m183/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2762 - loss: 1.9893
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2755 - loss: 1.9888
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2756 - loss: 1.9877
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2758 - loss: 1.9868 - val_accuracy: 0.3445 - val_loss: 1.7342
Epoch 35/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2897 - loss: 1.9470 
[1m 70/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2912 - loss: 1.9409
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2896 - loss: 1.9437
[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2893 - loss: 1.9438
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[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2889 - loss: 1.9461
[1m263/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2888 - loss: 1.9469
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Epoch 36/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2788 - loss: 1.9643 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2847 - loss: 1.9544
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2898 - loss: 1.9472 - val_accuracy: 0.3436 - val_loss: 1.7130
Epoch 37/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1875 - loss: 2.1607
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2858 - loss: 1.9652 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2922 - loss: 1.9569
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Epoch 38/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3071 - loss: 1.9345 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2996 - loss: 1.9445
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2986 - loss: 1.9415
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2985 - loss: 1.9406
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2984 - loss: 1.9401 - val_accuracy: 0.3541 - val_loss: 1.6879
Epoch 39/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2344 - loss: 2.0440
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2920 - loss: 1.9349 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2909 - loss: 1.9353
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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2911 - loss: 1.9351
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2918 - loss: 1.9333
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2926 - loss: 1.9323
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2930 - loss: 1.9315
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2933 - loss: 1.9310 - val_accuracy: 0.3530 - val_loss: 1.6906
Epoch 40/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3281 - loss: 1.9707
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3149 - loss: 1.9004 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3094 - loss: 1.9046
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[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3050 - loss: 1.9082
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3037 - loss: 1.9087
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3031 - loss: 1.9091
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3024 - loss: 1.9101
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3022 - loss: 1.9106 - val_accuracy: 0.3539 - val_loss: 1.6812
Epoch 41/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.4062 - loss: 1.7118
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3047 - loss: 1.8959 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3036 - loss: 1.9005
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[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3014 - loss: 1.9020
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3016 - loss: 1.9021
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3016 - loss: 1.9022
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Epoch 42/110

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Epoch 43/110

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Epoch 44/110

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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3057 - loss: 1.8831 - val_accuracy: 0.3536 - val_loss: 1.6693
Epoch 45/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3101 - loss: 1.9263 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3158 - loss: 1.9038
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[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3172 - loss: 1.8866
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3167 - loss: 1.8853
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Epoch 46/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3228 - loss: 1.9070 
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3160 - loss: 1.8853
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Epoch 47/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3060 - loss: 1.8975 
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3149 - loss: 1.8710
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Epoch 48/110

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Epoch 49/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3195 - loss: 1.8697 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3170 - loss: 1.8688
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[1m237/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3149 - loss: 1.8606
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3146 - loss: 1.8600 - val_accuracy: 0.3584 - val_loss: 1.6440
Epoch 50/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.3281 - loss: 1.9799
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3006 - loss: 1.8711 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3057 - loss: 1.8654
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[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3102 - loss: 1.8573
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3112 - loss: 1.8560
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3121 - loss: 1.8554
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3125 - loss: 1.8551 - val_accuracy: 0.3748 - val_loss: 1.6345
Epoch 51/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3242 - loss: 1.8327 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3209 - loss: 1.8329
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3221 - loss: 1.8341
[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3227 - loss: 1.8350
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3234 - loss: 1.8360
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3236 - loss: 1.8368
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3239 - loss: 1.8371
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3240 - loss: 1.8372 - val_accuracy: 0.3634 - val_loss: 1.6431
Epoch 52/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.2969 - loss: 1.7577
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3248 - loss: 1.8320 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3209 - loss: 1.8438
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3190 - loss: 1.8502
[1m147/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3191 - loss: 1.8514
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3200 - loss: 1.8507
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3209 - loss: 1.8494
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3215 - loss: 1.8484
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Epoch 53/110

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Epoch 54/110

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Epoch 55/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3100 - loss: 1.8367 
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3242 - loss: 1.8271
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3246 - loss: 1.8279
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3248 - loss: 1.8283 - val_accuracy: 0.3689 - val_loss: 1.6278
Epoch 56/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3472 - loss: 1.8032 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3400 - loss: 1.8100
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[1m180/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3358 - loss: 1.8138
[1m219/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3349 - loss: 1.8154
[1m254/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3342 - loss: 1.8169
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3338 - loss: 1.8180 - val_accuracy: 0.3697 - val_loss: 1.6289
Epoch 57/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3430 - loss: 1.7933 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3398 - loss: 1.7969
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[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3351 - loss: 1.8050
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[1m241/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3330 - loss: 1.8092
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Epoch 58/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3116 - loss: 1.8403 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3145 - loss: 1.8314
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Epoch 59/110

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Epoch 60/110

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Epoch 61/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3384 - loss: 1.8041 
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3407 - loss: 1.7914
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3398 - loss: 1.7926
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3394 - loss: 1.7932 - val_accuracy: 0.3741 - val_loss: 1.6140
Epoch 62/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3468 - loss: 1.8015 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3407 - loss: 1.7964
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[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3393 - loss: 1.7941
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3392 - loss: 1.7938
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3393 - loss: 1.7935
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3393 - loss: 1.7934 - val_accuracy: 0.3843 - val_loss: 1.6138
Epoch 63/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3471 - loss: 1.7862 
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3388 - loss: 1.7911
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3384 - loss: 1.7917
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Epoch 64/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3594 - loss: 2.0304
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[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3269 - loss: 1.8312
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3294 - loss: 1.8223
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3304 - loss: 1.8188
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3310 - loss: 1.8166 - val_accuracy: 0.3837 - val_loss: 1.6147
Epoch 65/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3125 - loss: 1.8080
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Epoch 66/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.4219 - loss: 1.8700
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[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3355 - loss: 1.7987
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3361 - loss: 1.7972
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3364 - loss: 1.7960 - val_accuracy: 0.3700 - val_loss: 1.6083
Epoch 67/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2969 - loss: 2.0021
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[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3332 - loss: 1.7864
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[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3347 - loss: 1.7840
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3346 - loss: 1.7844
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3350 - loss: 1.7840
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3352 - loss: 1.7841 - val_accuracy: 0.3800 - val_loss: 1.6089
Epoch 68/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.3750 - loss: 1.7050
[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3403 - loss: 1.8090 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3418 - loss: 1.7965
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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3421 - loss: 1.7873
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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:12[0m 851ms/step
[1m62/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 828us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 9ms/step
Saved model to disk.

Accuracy capturado en la ejecución 17: 35.0 [%]
F1-score capturado en la ejecución 17: 34.26 [%]

=== EJECUCIÓN 18 ===

--- TRAIN (ejecución 18) ---

--- TEST (ejecución 18) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18676, 6, 250)

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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m66/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 776us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step  
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[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 63/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 812us/step
[1m138/169[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 735us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 37.3 [%]
Global F1 score (validation) = 36.76 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[1.8949731e-01 1.9820529e-01 2.0103110e-01 ... 6.0762432e-06
  1.8493195e-01 1.1562136e-02]
 [2.0659831e-01 2.0029430e-01 2.1821943e-01 ... 9.7579050e-06
  1.4735468e-01 1.0370499e-02]
 [1.8485175e-01 1.8253641e-01 1.9283982e-01 ... 1.6518063e-05
  2.0218717e-01 3.8163908e-02]
 ...
 [1.7958689e-01 1.9496989e-01 1.9456600e-01 ... 4.0377997e-04
  1.7169873e-01 2.0567317e-02]
 [1.9396183e-01 2.0253156e-01 2.0059408e-01 ... 2.7122442e-05
  1.7306378e-01 1.2115735e-02]
 [4.2982947e-02 6.4686507e-02 4.6394974e-02 ... 4.2414414e-03
  6.0590412e-02 2.5213279e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 43.48 [%]
Global accuracy score (test) = 34.53 [%]
Global F1 score (train) = 42.6 [%]
Global F1 score (test) = 34.11 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.22      0.26      0.24       184
 CAMINAR CON MÓVIL O LIBRO       0.24      0.39      0.30       184
       CAMINAR USUAL SPEED       0.25      0.17      0.20       184
            CAMINAR ZIGZAG       0.24      0.20      0.21       184
          DE PIE BARRIENDO       0.20      0.41      0.27       184
   DE PIE DOBLANDO TOALLAS       0.29      0.11      0.16       184
    DE PIE MOVIENDO LIBROS       0.26      0.33      0.29       184
          DE PIE USANDO PC       0.39      0.60      0.48       184
        FASE REPOSO CON K5       0.65      0.78      0.71       184
INCREMENTAL CICLOERGOMETRO       0.86      0.60      0.71       184
           SENTADO LEYENDO       0.24      0.20      0.22       184
         SENTADO USANDO PC       0.09      0.05      0.06       184
      SENTADO VIENDO LA TV       0.30      0.28      0.29       184
   SUBIR Y BAJAR ESCALERAS       0.29      0.15      0.19       184
                    TROTAR       0.88      0.71      0.79       161

                  accuracy                           0.35      2737
                 macro avg       0.36      0.35      0.34      2737
              weighted avg       0.36      0.35      0.34      2737

2025-10-28 14:21:12.839942: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 14:21:12.851435: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1761657672.865027 2352690 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761657672.869344 2352690 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761657672.879280 2352690 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657672.879302 2352690 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657672.879305 2352690 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657672.879306 2352690 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 14:21:12.882599: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1761657675.260407 2352690 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13733 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/110
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761657676.971945 2352802 service.cc:152] XLA service 0x7ec55c00c640 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761657676.971982 2352802 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 14:21:17.005635: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
I0000 00:00:1761657677.176383 2352802 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761657679.473993 2352802 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16:53[0m 3s/step - accuracy: 0.1406 - loss: 3.1775
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[1m144/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0763 - loss: 3.4894
[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0766 - loss: 3.4819
[1m217/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0769 - loss: 3.4763
[1m258/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0773 - loss: 3.4681
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step - accuracy: 0.0776 - loss: 3.4609
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 16ms/step - accuracy: 0.0776 - loss: 3.4607 - val_accuracy: 0.1558 - val_loss: 2.5127
Epoch 2/110

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Epoch 3/110

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Epoch 4/110

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Epoch 5/110

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Epoch 6/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1495 - loss: 2.7757 
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Epoch 7/110

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[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1511 - loss: 2.6741
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1513 - loss: 2.6736
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1516 - loss: 2.6721
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1517 - loss: 2.6714 - val_accuracy: 0.1939 - val_loss: 2.2693
Epoch 8/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1875 - loss: 2.6858
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1723 - loss: 2.6349 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1694 - loss: 2.6257
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1682 - loss: 2.6202
[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1671 - loss: 2.6176
[1m197/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1662 - loss: 2.6155
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1651 - loss: 2.6145
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1643 - loss: 2.6132
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1640 - loss: 2.6126 - val_accuracy: 0.2122 - val_loss: 2.2171
Epoch 9/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2031 - loss: 2.6308
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1677 - loss: 2.5916 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1642 - loss: 2.5870
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1621 - loss: 2.5842
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1609 - loss: 2.5810
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1609 - loss: 2.5776
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1612 - loss: 2.5745
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1616 - loss: 2.5716
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1619 - loss: 2.5694 - val_accuracy: 0.2161 - val_loss: 2.1792
Epoch 10/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2188 - loss: 2.5481
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1790 - loss: 2.5197 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1752 - loss: 2.5129
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1740 - loss: 2.5104
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1734 - loss: 2.5079
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1734 - loss: 2.5059
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1730 - loss: 2.5048
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1728 - loss: 2.5033
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1728 - loss: 2.5023 - val_accuracy: 0.2278 - val_loss: 2.1372
Epoch 11/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1406 - loss: 2.5027
[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1718 - loss: 2.4338 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1711 - loss: 2.4431
[1m121/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1711 - loss: 2.4464
[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1714 - loss: 2.4477
[1m198/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1723 - loss: 2.4474
[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1732 - loss: 2.4461
[1m275/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1740 - loss: 2.4448
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1744 - loss: 2.4440 - val_accuracy: 0.2311 - val_loss: 2.0946
Epoch 12/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.2188 - loss: 2.3003
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1863 - loss: 2.4049 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1888 - loss: 2.4016
[1m111/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1903 - loss: 2.3998
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1903 - loss: 2.3992
[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1898 - loss: 2.3986
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1893 - loss: 2.3978
[1m262/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1890 - loss: 2.3972
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1887 - loss: 2.3969 - val_accuracy: 0.2383 - val_loss: 2.0698
Epoch 13/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1858 - loss: 2.3547 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1867 - loss: 2.3543
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[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1884 - loss: 2.3486
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Epoch 14/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2344 - loss: 2.1784
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2242 - loss: 2.2672 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2129 - loss: 2.2927
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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2011 - loss: 2.3126
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2004 - loss: 2.3128
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2000 - loss: 2.3127 - val_accuracy: 0.2463 - val_loss: 2.0153
Epoch 15/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2188 - loss: 2.2325
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2014 - loss: 2.2966 
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[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2061 - loss: 2.2845
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2062 - loss: 2.2842
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2062 - loss: 2.2837
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2061 - loss: 2.2832 - val_accuracy: 0.2525 - val_loss: 1.9803
Epoch 16/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.1406 - loss: 2.2814
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2024 - loss: 2.2465 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2064 - loss: 2.2536
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2077 - loss: 2.2546
[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2084 - loss: 2.2558
[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2086 - loss: 2.2569
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2091 - loss: 2.2565
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2093 - loss: 2.2553
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2093 - loss: 2.2545 - val_accuracy: 0.2642 - val_loss: 1.9591
Epoch 17/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2656 - loss: 2.2690
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2238 - loss: 2.2402 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2236 - loss: 2.2308
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2231 - loss: 2.2260
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[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2212 - loss: 2.2224
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2204 - loss: 2.2215
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2196 - loss: 2.2209
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Epoch 18/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2038 - loss: 2.2443 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2023 - loss: 2.2383
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[1m199/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2076 - loss: 2.2224
[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2089 - loss: 2.2200
[1m276/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2102 - loss: 2.2177
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2106 - loss: 2.2170 - val_accuracy: 0.2866 - val_loss: 1.9150
Epoch 19/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2344 - loss: 2.2572
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2296 - loss: 2.1740 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2253 - loss: 2.1770
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2226 - loss: 2.1798
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2211 - loss: 2.1806
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2204 - loss: 2.1806
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2201 - loss: 2.1799
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2199 - loss: 2.1793
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2198 - loss: 2.1790 - val_accuracy: 0.2810 - val_loss: 1.9002
Epoch 20/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3125 - loss: 2.1155
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2197 - loss: 2.1805 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2160 - loss: 2.1841
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2153 - loss: 2.1816
[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2157 - loss: 2.1783
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2166 - loss: 2.1750
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2172 - loss: 2.1722
[1m258/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2176 - loss: 2.1700
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2180 - loss: 2.1681 - val_accuracy: 0.2714 - val_loss: 1.8898
Epoch 21/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1875 - loss: 2.1655
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2364 - loss: 2.1271 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2390 - loss: 2.1266
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2379 - loss: 2.1263
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2361 - loss: 2.1273
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2351 - loss: 2.1279
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2346 - loss: 2.1275
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2342 - loss: 2.1271
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2339 - loss: 2.1270 - val_accuracy: 0.2779 - val_loss: 1.8951
Epoch 22/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1562 - loss: 2.1790
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2381 - loss: 2.1080 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2354 - loss: 2.1162
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2341 - loss: 2.1219
[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2330 - loss: 2.1258
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2325 - loss: 2.1278
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2325 - loss: 2.1278
[1m275/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2328 - loss: 2.1269
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2329 - loss: 2.1264 - val_accuracy: 0.3058 - val_loss: 1.8564
Epoch 23/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1875 - loss: 2.2537
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2576 - loss: 2.0938 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2482 - loss: 2.0979
[1m120/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2446 - loss: 2.0998
[1m160/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2430 - loss: 2.0998
[1m200/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2421 - loss: 2.0996
[1m241/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2418 - loss: 2.0996
[1m281/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2416 - loss: 2.0993
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Epoch 24/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2812 - loss: 2.0540
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2415 - loss: 2.0951 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2482 - loss: 2.0834
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2456 - loss: 2.0849
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2449 - loss: 2.0854 - val_accuracy: 0.2884 - val_loss: 1.8421
Epoch 25/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2500 - loss: 2.0351
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2406 - loss: 2.0876 
[1m 70/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2442 - loss: 2.0849
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[1m181/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2476 - loss: 2.0817
[1m220/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2479 - loss: 2.0814
[1m259/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2480 - loss: 2.0810
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2481 - loss: 2.0803 - val_accuracy: 0.2871 - val_loss: 1.8391
Epoch 26/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2344 - loss: 2.2803
[1m 34/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2392 - loss: 2.1427 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2422 - loss: 2.1088
[1m109/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2452 - loss: 2.0911
[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2471 - loss: 2.0820
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2491 - loss: 2.0760
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2502 - loss: 2.0727
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2506 - loss: 2.0710
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2508 - loss: 2.0700 - val_accuracy: 0.2982 - val_loss: 1.8299
Epoch 27/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.1719 - loss: 2.0334
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2470 - loss: 2.1046 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2515 - loss: 2.0915
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2510 - loss: 2.0828
[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2499 - loss: 2.0773
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2496 - loss: 2.0735
[1m237/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2496 - loss: 2.0706
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2496 - loss: 2.0685
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2497 - loss: 2.0672 - val_accuracy: 0.3143 - val_loss: 1.8132
Epoch 28/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2969 - loss: 1.9158
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2543 - loss: 2.0446 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2543 - loss: 2.0430
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2537 - loss: 2.0433
[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2531 - loss: 2.0436
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2531 - loss: 2.0428
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2528 - loss: 2.0424
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2529 - loss: 2.0415
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2531 - loss: 2.0407 - val_accuracy: 0.3145 - val_loss: 1.8171
Epoch 29/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2656 - loss: 1.9997
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2470 - loss: 2.0002 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2502 - loss: 2.0089
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[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2523 - loss: 2.0171
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Epoch 30/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2778 - loss: 2.0060 
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Epoch 31/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2687 - loss: 2.0039 
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[1m255/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2662 - loss: 2.0139
[1m291/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2659 - loss: 2.0136
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2659 - loss: 2.0136 - val_accuracy: 0.2955 - val_loss: 1.7956
Epoch 32/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2745 - loss: 2.0167 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2674 - loss: 2.0164
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2648 - loss: 2.0101
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2640 - loss: 2.0094
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2635 - loss: 2.0096
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2636 - loss: 2.0090
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2637 - loss: 2.0086 - val_accuracy: 0.3227 - val_loss: 1.7788
Epoch 33/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2592 - loss: 2.0130 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2636 - loss: 2.0016
[1m109/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2660 - loss: 1.9972
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2687 - loss: 1.9942
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2695 - loss: 1.9940
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2699 - loss: 1.9942
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2699 - loss: 1.9942
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2697 - loss: 1.9943 - val_accuracy: 0.3343 - val_loss: 1.7685
Epoch 34/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2188 - loss: 1.9208
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2799 - loss: 1.9923 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2741 - loss: 1.9868
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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2723 - loss: 1.9822
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[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2721 - loss: 1.9799
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2720 - loss: 1.9798 - val_accuracy: 0.3221 - val_loss: 1.7701
Epoch 35/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2511 - loss: 2.0038 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2601 - loss: 1.9938
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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2690 - loss: 1.9804
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Epoch 36/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3033 - loss: 1.9550 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2946 - loss: 1.9571
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[1m239/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2887 - loss: 1.9570
[1m279/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2887 - loss: 1.9569
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2887 - loss: 1.9569 - val_accuracy: 0.3197 - val_loss: 1.7705
Epoch 37/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2344 - loss: 1.9757
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2747 - loss: 1.9649 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2804 - loss: 1.9598
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[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2813 - loss: 1.9588
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2822 - loss: 1.9566
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2828 - loss: 1.9564
[1m258/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2833 - loss: 1.9562
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2837 - loss: 1.9560 - val_accuracy: 0.3391 - val_loss: 1.7509
Epoch 38/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2656 - loss: 1.9393
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2874 - loss: 1.9965 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2859 - loss: 1.9862
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2874 - loss: 1.9776
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2887 - loss: 1.9721
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2895 - loss: 1.9684
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2897 - loss: 1.9656
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2895 - loss: 1.9629
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2891 - loss: 1.9615 - val_accuracy: 0.3528 - val_loss: 1.7345
Epoch 39/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3281 - loss: 1.8282
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2825 - loss: 1.9317 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2861 - loss: 1.9435
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2860 - loss: 1.9449
[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2861 - loss: 1.9463
[1m183/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2857 - loss: 1.9482
[1m222/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2854 - loss: 1.9488
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2852 - loss: 1.9490
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2853 - loss: 1.9487 - val_accuracy: 0.3360 - val_loss: 1.7332
Epoch 40/110

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Epoch 41/110

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Epoch 42/110

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Epoch 43/110

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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2969 - loss: 1.9290
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2975 - loss: 1.9266
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Epoch 44/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3070 - loss: 1.9078 
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Epoch 45/110

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Epoch 46/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3164 - loss: 1.9007 
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Epoch 47/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3073 - loss: 1.8803 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3070 - loss: 1.8787
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[1m254/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3077 - loss: 1.8745
[1m291/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3079 - loss: 1.8745
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Epoch 48/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3200 - loss: 1.8660 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3185 - loss: 1.8685
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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3159 - loss: 1.8740
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3152 - loss: 1.8750
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3151 - loss: 1.8750
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3151 - loss: 1.8754
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3150 - loss: 1.8755 - val_accuracy: 0.3630 - val_loss: 1.6730
Epoch 49/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3097 - loss: 1.8594 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3088 - loss: 1.8619
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3086 - loss: 1.8696
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3092 - loss: 1.8702
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3094 - loss: 1.8706
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3095 - loss: 1.8713
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3094 - loss: 1.8719 - val_accuracy: 0.3623 - val_loss: 1.6617
Epoch 50/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3253 - loss: 1.8820 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3247 - loss: 1.8781
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[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3223 - loss: 1.8746
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3214 - loss: 1.8738
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3211 - loss: 1.8734
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3210 - loss: 1.8734
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Epoch 51/110

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Epoch 52/110

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Epoch 53/110

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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3156 - loss: 1.8336 - val_accuracy: 0.3697 - val_loss: 1.6525
Epoch 54/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3222 - loss: 1.8277 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3237 - loss: 1.8339
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[1m239/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3237 - loss: 1.8398
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3237 - loss: 1.8406 - val_accuracy: 0.3495 - val_loss: 1.6543
Epoch 55/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3093 - loss: 1.8760 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3148 - loss: 1.8642
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[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3185 - loss: 1.8498
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Epoch 56/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3278 - loss: 1.8242 
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Epoch 57/110

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Epoch 58/110

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Epoch 59/110

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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3282 - loss: 1.8129 - val_accuracy: 0.3695 - val_loss: 1.6416
Epoch 60/110

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[1m 34/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3351 - loss: 1.8356 
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Epoch 61/110

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Epoch 62/110

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Epoch 63/110

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Epoch 64/110

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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3360 - loss: 1.7819
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Epoch 65/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3569 - loss: 1.8292 
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[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3380 - loss: 1.8166
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Epoch 66/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3444 - loss: 1.7637 
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Epoch 67/110

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Epoch 68/110

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Epoch 69/110

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Epoch 70/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3380 - loss: 1.7268 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3337 - loss: 1.7417
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3338 - loss: 1.7642
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3338 - loss: 1.7677 - val_accuracy: 0.3739 - val_loss: 1.6168
Epoch 71/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3472 - loss: 1.7541 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3481 - loss: 1.7606
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[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3479 - loss: 1.7657
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3472 - loss: 1.7681
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3465 - loss: 1.7696
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Epoch 72/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3430 - loss: 1.7822 
[1m 67/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3408 - loss: 1.7792
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Epoch 73/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3320 - loss: 1.8278 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3343 - loss: 1.8064
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Epoch 74/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3333 - loss: 1.7835 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3363 - loss: 1.7770
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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3391 - loss: 1.7736
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3387 - loss: 1.7736
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3390 - loss: 1.7729
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3394 - loss: 1.7724 - val_accuracy: 0.3630 - val_loss: 1.6081
Epoch 75/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3338 - loss: 1.7788 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3406 - loss: 1.7713
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3428 - loss: 1.7723
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3436 - loss: 1.7719
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3440 - loss: 1.7703
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3441 - loss: 1.7691
[1m262/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3444 - loss: 1.7679
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3444 - loss: 1.7674 - val_accuracy: 0.3597 - val_loss: 1.6077
Epoch 76/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2969 - loss: 1.9563
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3448 - loss: 1.7712 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3458 - loss: 1.7657
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3452 - loss: 1.7617
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3450 - loss: 1.7602
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3452 - loss: 1.7597
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3455 - loss: 1.7601
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3459 - loss: 1.7607
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3460 - loss: 1.7608 - val_accuracy: 0.3700 - val_loss: 1.6042
Epoch 77/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2656 - loss: 1.9146
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3397 - loss: 1.8077 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3434 - loss: 1.7867
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3438 - loss: 1.7792
[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3441 - loss: 1.7749
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3450 - loss: 1.7711
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3456 - loss: 1.7686
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3460 - loss: 1.7674
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3462 - loss: 1.7668 - val_accuracy: 0.3676 - val_loss: 1.6078
Epoch 78/110

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Epoch 79/110

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Epoch 80/110

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[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3447 - loss: 1.7478
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3449 - loss: 1.7472 - val_accuracy: 0.3771 - val_loss: 1.6090
Epoch 81/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3536 - loss: 1.7329 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3543 - loss: 1.7383
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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3515 - loss: 1.7418
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3500 - loss: 1.7431
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3488 - loss: 1.7442
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3482 - loss: 1.7449
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3478 - loss: 1.7450 - val_accuracy: 0.3697 - val_loss: 1.6041
Epoch 82/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3475 - loss: 1.7718 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3484 - loss: 1.7558
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3468 - loss: 1.7528
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[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3465 - loss: 1.7461
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Epoch 83/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3210 - loss: 1.7583 
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Epoch 84/110

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Epoch 85/110

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[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3457 - loss: 1.7425
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Epoch 86/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3464 - loss: 1.7422 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3501 - loss: 1.7403
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[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3504 - loss: 1.7407
[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3505 - loss: 1.7400
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3506 - loss: 1.7397
[1m262/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3506 - loss: 1.7397
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3506 - loss: 1.7397 - val_accuracy: 0.3700 - val_loss: 1.6000
Epoch 87/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3438 - loss: 1.7763
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3452 - loss: 1.7212 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3509 - loss: 1.7195
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3519 - loss: 1.7190
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3528 - loss: 1.7194
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3530 - loss: 1.7200
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3530 - loss: 1.7208
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3532 - loss: 1.7213
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3533 - loss: 1.7216 - val_accuracy: 0.3721 - val_loss: 1.6078
Epoch 88/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3513 - loss: 1.7819 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3512 - loss: 1.7657
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3504 - loss: 1.7585
[1m146/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3508 - loss: 1.7527
[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3511 - loss: 1.7490
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3513 - loss: 1.7467
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Epoch 89/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3328 - loss: 1.7857 
[1m 70/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3373 - loss: 1.7772
[1m108/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3402 - loss: 1.7726
[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3426 - loss: 1.7661
[1m182/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3439 - loss: 1.7616
[1m219/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3448 - loss: 1.7579
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Epoch 90/110

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[1m108/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3483 - loss: 1.7437
[1m146/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3480 - loss: 1.7407
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[1m220/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3485 - loss: 1.7377
[1m257/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3485 - loss: 1.7376
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3485 - loss: 1.7375 - val_accuracy: 0.3752 - val_loss: 1.6010

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[1m66/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 770us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
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Saved model to disk.

Accuracy capturado en la ejecución 18: 34.53 [%]
F1-score capturado en la ejecución 18: 34.11 [%]

=== EJECUCIÓN 19 ===

--- TRAIN (ejecución 19) ---

--- TEST (ejecución 19) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18676, 6, 250)

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[1m 60/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 854us/step  
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

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[1m64/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 798us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 8ms/step

[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 72/169[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 714us/step
[1m147/169[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 693us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 37.52 [%]
Global F1 score (validation) = 37.4 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[2.1617872e-01 1.8682598e-01 2.1854724e-01 ... 7.1082673e-06
  1.3838698e-01 2.3790605e-02]
 [2.1351953e-01 1.8841569e-01 2.1955951e-01 ... 1.0325444e-05
  1.4208168e-01 2.3930643e-02]
 [2.1145102e-01 1.9236144e-01 2.1455826e-01 ... 1.4278749e-05
  1.4525808e-01 8.6001344e-03]
 ...
 [1.9420208e-01 1.9510813e-01 2.0497371e-01 ... 6.4635860e-05
  1.7088276e-01 2.0190874e-02]
 [1.8968768e-01 2.1267840e-01 2.0669906e-01 ... 2.3470244e-05
  1.6818777e-01 5.4132231e-03]
 [5.4794505e-02 7.2604775e-02 5.9865680e-02 ... 1.5541459e-03
  7.9735301e-02 1.4177399e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 44.21 [%]
Global accuracy score (test) = 36.39 [%]
Global F1 score (train) = 43.46 [%]
Global F1 score (test) = 35.93 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.29      0.16      0.20       184
 CAMINAR CON MÓVIL O LIBRO       0.22      0.18      0.20       184
       CAMINAR USUAL SPEED       0.22      0.40      0.28       184
            CAMINAR ZIGZAG       0.16      0.20      0.18       184
          DE PIE BARRIENDO       0.25      0.51      0.34       184
   DE PIE DOBLANDO TOALLAS       0.30      0.21      0.25       184
    DE PIE MOVIENDO LIBROS       0.32      0.29      0.30       184
          DE PIE USANDO PC       0.42      0.60      0.50       184
        FASE REPOSO CON K5       0.69      0.80      0.74       184
INCREMENTAL CICLOERGOMETRO       0.87      0.61      0.72       184
           SENTADO LEYENDO       0.35      0.36      0.36       184
         SENTADO USANDO PC       0.15      0.08      0.10       184
      SENTADO VIENDO LA TV       0.31      0.27      0.29       184
   SUBIR Y BAJAR ESCALERAS       0.35      0.12      0.18       184
                    TROTAR       0.81      0.71      0.76       161

                  accuracy                           0.36      2737
                 macro avg       0.38      0.37      0.36      2737
              weighted avg       0.38      0.36      0.36      2737

2025-10-28 14:22:26.086572: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 14:22:26.097869: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1761657746.111049 2362102 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761657746.115430 2362102 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761657746.125256 2362102 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657746.125276 2362102 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657746.125278 2362102 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657746.125280 2362102 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 14:22:26.128574: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1761657748.513358 2362102 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13733 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/110
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761657750.234339 2362221 service.cc:152] XLA service 0x709e8001e600 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761657750.234374 2362221 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 14:22:30.267035: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
I0000 00:00:1761657750.441137 2362221 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761657752.730992 2362221 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/110

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Epoch 3/110

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Epoch 4/110

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Epoch 5/110

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Epoch 6/110

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Epoch 7/110

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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1510 - loss: 2.6853 - val_accuracy: 0.2276 - val_loss: 2.2412
Epoch 8/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1766 - loss: 2.5837 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1703 - loss: 2.5921
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[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1676 - loss: 2.5949
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1666 - loss: 2.5950
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1659 - loss: 2.5948
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1655 - loss: 2.5946
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1654 - loss: 2.5940 - val_accuracy: 0.2300 - val_loss: 2.2070
Epoch 9/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1591 - loss: 2.5544 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1622 - loss: 2.5523
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[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1656 - loss: 2.5467
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1662 - loss: 2.5456
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1668 - loss: 2.5447
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1674 - loss: 2.5434
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Epoch 10/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1892 - loss: 2.4991 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1888 - loss: 2.4864
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[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1824 - loss: 2.4820
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1820 - loss: 2.4815 - val_accuracy: 0.2533 - val_loss: 2.1115
Epoch 11/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2500 - loss: 2.3854
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1950 - loss: 2.4594 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1884 - loss: 2.4535
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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1862 - loss: 2.4405
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1864 - loss: 2.4369 - val_accuracy: 0.2574 - val_loss: 2.0903
Epoch 12/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1719 - loss: 2.4332
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1825 - loss: 2.3975 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1816 - loss: 2.4010
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1810 - loss: 2.3997
[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1817 - loss: 2.3975
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1829 - loss: 2.3952
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1842 - loss: 2.3932
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1851 - loss: 2.3914
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1856 - loss: 2.3903 - val_accuracy: 0.2625 - val_loss: 2.0644
Epoch 13/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1875 - loss: 2.4934
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1969 - loss: 2.3449 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1981 - loss: 2.3422
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1997 - loss: 2.3404
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1998 - loss: 2.3404
[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1993 - loss: 2.3411
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1994 - loss: 2.3413
[1m257/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1994 - loss: 2.3410
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1996 - loss: 2.3403 - val_accuracy: 0.2659 - val_loss: 2.0124
Epoch 14/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2500 - loss: 2.2601
[1m 30/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1989 - loss: 2.3112 
[1m 69/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1919 - loss: 2.3216
[1m107/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1934 - loss: 2.3192
[1m146/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1947 - loss: 2.3151
[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1957 - loss: 2.3113
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1966 - loss: 2.3092
[1m262/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1973 - loss: 2.3077
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1980 - loss: 2.3064 - val_accuracy: 0.2877 - val_loss: 1.9874
Epoch 15/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.2188 - loss: 2.1413
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2092 - loss: 2.2555 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2120 - loss: 2.2531
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2125 - loss: 2.2555
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2122 - loss: 2.2569
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2112 - loss: 2.2586
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2106 - loss: 2.2603
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2104 - loss: 2.2614
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2103 - loss: 2.2618 - val_accuracy: 0.2722 - val_loss: 1.9806
Epoch 16/110

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Epoch 17/110

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Epoch 18/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2382 - loss: 2.1673 
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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2244 - loss: 2.1832
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Epoch 19/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2184 - loss: 2.1951 
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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2252 - loss: 2.1803
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2255 - loss: 2.1801
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2255 - loss: 2.1798
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2257 - loss: 2.1789
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2259 - loss: 2.1782 - val_accuracy: 0.2999 - val_loss: 1.9062
Epoch 20/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2450 - loss: 2.1089 
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[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2404 - loss: 2.1312
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2395 - loss: 2.1328
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Epoch 21/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2487 - loss: 2.0939 
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[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2459 - loss: 2.1122
[1m256/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2449 - loss: 2.1143
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2443 - loss: 2.1156 - val_accuracy: 0.3075 - val_loss: 1.8680
Epoch 22/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2500 - loss: 2.0278
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2576 - loss: 2.0993 
[1m 70/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2554 - loss: 2.1065
[1m108/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2529 - loss: 2.1104
[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2518 - loss: 2.1121
[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2514 - loss: 2.1122
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2514 - loss: 2.1111
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2513 - loss: 2.1096 - val_accuracy: 0.3010 - val_loss: 1.8615
Epoch 23/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1562 - loss: 2.1546
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2286 - loss: 2.1243 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2343 - loss: 2.1184
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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2394 - loss: 2.1117
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2406 - loss: 2.1088
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2414 - loss: 2.1071
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2422 - loss: 2.1052
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2426 - loss: 2.1040 - val_accuracy: 0.3269 - val_loss: 1.8380
Epoch 24/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2812 - loss: 1.8978
[1m 34/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2426 - loss: 2.0673 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2428 - loss: 2.0797
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2442 - loss: 2.0823
[1m145/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2454 - loss: 2.0826
[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2467 - loss: 2.0816
[1m222/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2480 - loss: 2.0805
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2489 - loss: 2.0804
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2494 - loss: 2.0804 - val_accuracy: 0.3138 - val_loss: 1.8307
Epoch 25/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2812 - loss: 2.0916
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2587 - loss: 2.0294 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2581 - loss: 2.0409
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2586 - loss: 2.0448
[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2586 - loss: 2.0469
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2578 - loss: 2.0495
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2570 - loss: 2.0520
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2568 - loss: 2.0535
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2568 - loss: 2.0544 - val_accuracy: 0.3203 - val_loss: 1.8159
Epoch 26/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2500 - loss: 2.1382
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2812 - loss: 2.0380 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2765 - loss: 2.0419
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2730 - loss: 2.0483
[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2716 - loss: 2.0502
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2703 - loss: 2.0515
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2692 - loss: 2.0524
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2684 - loss: 2.0527
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2680 - loss: 2.0527 - val_accuracy: 0.3352 - val_loss: 1.8023
Epoch 27/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2717 - loss: 2.0128 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2673 - loss: 2.0211
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2667 - loss: 2.0296 - val_accuracy: 0.3273 - val_loss: 1.7927
Epoch 28/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2468 - loss: 2.0663 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2523 - loss: 2.0491
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[1m222/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2584 - loss: 2.0333
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2600 - loss: 2.0301 - val_accuracy: 0.3369 - val_loss: 1.7854
Epoch 29/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2947 - loss: 1.9670 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2884 - loss: 1.9754
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[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2828 - loss: 1.9872
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2820 - loss: 1.9903
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2813 - loss: 1.9933
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2807 - loss: 1.9955
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2801 - loss: 1.9970 - val_accuracy: 0.3328 - val_loss: 1.7716
Epoch 30/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2963 - loss: 1.9792 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2897 - loss: 1.9903
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2866 - loss: 1.9923
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2846 - loss: 1.9929
[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2837 - loss: 1.9929
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2828 - loss: 1.9938
[1m263/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2822 - loss: 1.9940
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2819 - loss: 1.9941 - val_accuracy: 0.3304 - val_loss: 1.7648
Epoch 31/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2812 - loss: 1.9460
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2809 - loss: 1.9775 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2835 - loss: 1.9752
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2832 - loss: 1.9798
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2832 - loss: 1.9824
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2833 - loss: 1.9840
[1m276/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2833 - loss: 1.9853
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2833 - loss: 1.9856 - val_accuracy: 0.3419 - val_loss: 1.7564
Epoch 32/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2835 - loss: 2.0079 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2840 - loss: 2.0078
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[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2830 - loss: 1.9994
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Epoch 33/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2833 - loss: 1.9730 
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Epoch 34/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2836 - loss: 1.9221 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2840 - loss: 1.9347
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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2862 - loss: 1.9503
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Epoch 35/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2826 - loss: 1.9438 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2845 - loss: 1.9443
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2837 - loss: 1.9509
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2841 - loss: 1.9509
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2843 - loss: 1.9508 - val_accuracy: 0.3447 - val_loss: 1.7128
Epoch 36/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2775 - loss: 1.9789 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2826 - loss: 1.9642
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[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2892 - loss: 1.9463
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2904 - loss: 1.9433
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2912 - loss: 1.9410
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2916 - loss: 1.9398 - val_accuracy: 0.3523 - val_loss: 1.7094
Epoch 37/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3041 - loss: 1.9263 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2988 - loss: 1.9315
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2981 - loss: 1.9302
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Epoch 38/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2970 - loss: 1.9098 
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Epoch 39/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3159 - loss: 1.8564 
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Epoch 40/110

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Epoch 41/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3021 - loss: 1.9057 
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3068 - loss: 1.8965
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3069 - loss: 1.8965
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3070 - loss: 1.8966 - val_accuracy: 0.3512 - val_loss: 1.6821
Epoch 42/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2796 - loss: 1.9251 
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[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3073 - loss: 1.8919
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Epoch 43/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3370 - loss: 1.8439 
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Epoch 44/110

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Epoch 45/110

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Epoch 46/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3135 - loss: 1.8473 
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3184 - loss: 1.8534
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3184 - loss: 1.8531
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3183 - loss: 1.8536 - val_accuracy: 0.3693 - val_loss: 1.6557
Epoch 47/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3112 - loss: 1.8826 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3186 - loss: 1.8688
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[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3196 - loss: 1.8595
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3197 - loss: 1.8593
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3196 - loss: 1.8586
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3195 - loss: 1.8583 - val_accuracy: 0.3556 - val_loss: 1.6568
Epoch 48/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3113 - loss: 1.8275 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3159 - loss: 1.8424
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[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3200 - loss: 1.8480
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Epoch 49/110

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Epoch 50/110

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Epoch 51/110

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Epoch 52/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3252 - loss: 1.8349 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3214 - loss: 1.8361
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[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3235 - loss: 1.8323
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Epoch 53/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3268 - loss: 1.7970 
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3224 - loss: 1.8180
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3225 - loss: 1.8189
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Epoch 54/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3239 - loss: 1.8478 
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Epoch 55/110

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Epoch 56/110

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Epoch 57/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3541 - loss: 1.8151 
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[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3408 - loss: 1.8057
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3395 - loss: 1.8068 - val_accuracy: 0.3599 - val_loss: 1.6264
Epoch 58/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3464 - loss: 1.7575 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3440 - loss: 1.7603
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[1m182/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3415 - loss: 1.7741
[1m222/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3411 - loss: 1.7773
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3408 - loss: 1.7796
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3405 - loss: 1.7818 - val_accuracy: 0.3593 - val_loss: 1.6380
Epoch 59/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3399 - loss: 1.7957 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3374 - loss: 1.7960
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3370 - loss: 1.8003
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Epoch 60/110

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Epoch 61/110

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Epoch 62/110

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Epoch 63/110

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Epoch 64/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3286 - loss: 1.8134 
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Epoch 65/110

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Epoch 66/110

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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3325 - loss: 1.7713
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Epoch 67/110

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Epoch 68/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3613 - loss: 1.7005 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3559 - loss: 1.7296
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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3515 - loss: 1.7552
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3513 - loss: 1.7572
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3511 - loss: 1.7581 - val_accuracy: 0.3747 - val_loss: 1.6132
Epoch 69/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3472 - loss: 1.7716 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3427 - loss: 1.7630
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[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3422 - loss: 1.7586
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3423 - loss: 1.7582
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3420 - loss: 1.7590
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3419 - loss: 1.7594
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Epoch 70/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3346 - loss: 1.7661 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3386 - loss: 1.7568
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[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3401 - loss: 1.7658
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Epoch 71/110

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Epoch 72/110

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Epoch 73/110

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Epoch 74/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3488 - loss: 1.7178 
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[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3447 - loss: 1.7457
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3444 - loss: 1.7469
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3443 - loss: 1.7478
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Epoch 75/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3614 - loss: 1.7263 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3611 - loss: 1.7367
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3597 - loss: 1.7429
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Epoch 76/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3475 - loss: 1.7340 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3503 - loss: 1.7317
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[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3520 - loss: 1.7348
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3520 - loss: 1.7350
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3523 - loss: 1.7345
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3525 - loss: 1.7347
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3525 - loss: 1.7351 - val_accuracy: 0.3798 - val_loss: 1.5941
Epoch 77/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3594 - loss: 1.5644
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3615 - loss: 1.7103 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3610 - loss: 1.7202
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3594 - loss: 1.7240
[1m146/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3583 - loss: 1.7260
[1m183/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3569 - loss: 1.7286
[1m218/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3564 - loss: 1.7305
[1m259/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3560 - loss: 1.7325
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3555 - loss: 1.7338 - val_accuracy: 0.3684 - val_loss: 1.5961
Epoch 78/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.3594 - loss: 1.6297
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3491 - loss: 1.7585 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3525 - loss: 1.7573
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3545 - loss: 1.7530
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3554 - loss: 1.7493
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3555 - loss: 1.7466
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3555 - loss: 1.7449
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3556 - loss: 1.7439
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3557 - loss: 1.7434 - val_accuracy: 0.3711 - val_loss: 1.6042

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:11[0m 842ms/step
[1m70/86[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 731us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 9ms/step
Saved model to disk.

Accuracy capturado en la ejecución 19: 36.39 [%]
F1-score capturado en la ejecución 19: 35.93 [%]

=== EJECUCIÓN 20 ===

--- TRAIN (ejecución 20) ---

--- TEST (ejecución 20) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18676, 6, 250)

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:44[0m 900ms/step
[1m 64/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 804us/step  
[1m133/584[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 767us/step
[1m207/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 735us/step
[1m278/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 728us/step
[1m352/584[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 717us/step
[1m427/584[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 708us/step
[1m499/584[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 707us/step
[1m572/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 705us/step
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m64/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 801us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 67/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 760us/step
[1m141/169[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 722us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 37.11 [%]
Global F1 score (validation) = 36.32 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[2.1053758e-01 2.0428461e-01 2.0425352e-01 ... 4.0003979e-06
  1.5225169e-01 1.0816201e-02]
 [2.1503019e-01 1.9279090e-01 2.1155922e-01 ... 6.6514076e-06
  1.5921783e-01 1.5087177e-02]
 [1.9501825e-01 1.9624026e-01 2.1560444e-01 ... 8.1921971e-06
  1.9402355e-01 1.4576072e-02]
 ...
 [1.9432904e-01 2.0323576e-01 1.9978280e-01 ... 1.6450546e-04
  1.7613311e-01 1.1801851e-02]
 [1.8804806e-01 2.1235271e-01 1.8976615e-01 ... 4.7184669e-05
  1.8249872e-01 7.2476966e-03]
 [6.0501579e-02 8.3425291e-02 6.4566419e-02 ... 4.1326256e-03
  1.0027665e-01 4.9167047e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 44.61 [%]
Global accuracy score (test) = 36.5 [%]
Global F1 score (train) = 43.25 [%]
Global F1 score (test) = 36.17 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.25      0.36      0.30       184
 CAMINAR CON MÓVIL O LIBRO       0.22      0.30      0.25       184
       CAMINAR USUAL SPEED       0.21      0.19      0.20       184
            CAMINAR ZIGZAG       0.28      0.13      0.18       184
          DE PIE BARRIENDO       0.21      0.48      0.29       184
   DE PIE DOBLANDO TOALLAS       0.35      0.11      0.17       184
    DE PIE MOVIENDO LIBROS       0.30      0.39      0.34       184
          DE PIE USANDO PC       0.41      0.60      0.48       184
        FASE REPOSO CON K5       0.65      0.78      0.71       184
INCREMENTAL CICLOERGOMETRO       0.92      0.62      0.74       184
           SENTADO LEYENDO       0.34      0.39      0.36       184
         SENTADO USANDO PC       0.10      0.06      0.08       184
      SENTADO VIENDO LA TV       0.39      0.25      0.30       184
   SUBIR Y BAJAR ESCALERAS       0.35      0.14      0.20       184
                    TROTAR       0.95      0.71      0.82       161

                  accuracy                           0.36      2737
                 macro avg       0.40      0.37      0.36      2737
              weighted avg       0.39      0.36      0.36      2737

2025-10-28 14:23:32.643276: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 14:23:32.654692: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1761657812.667877 2370379 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761657812.672116 2370379 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761657812.681925 2370379 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657812.681945 2370379 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657812.681947 2370379 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657812.681948 2370379 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 14:23:32.685200: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1761657815.030773 2370379 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13733 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/110
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761657816.712458 2370513 service.cc:152] XLA service 0x7db0200051e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761657816.712539 2370513 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 14:23:36.746540: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
I0000 00:00:1761657816.910609 2370513 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761657819.226935 2370513 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/110

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Epoch 3/110

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Epoch 4/110

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Epoch 5/110

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Epoch 6/110

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Epoch 7/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1618 - loss: 2.6651 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1528 - loss: 2.6824
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1459 - loss: 2.6924
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1451 - loss: 2.6902 - val_accuracy: 0.2026 - val_loss: 2.3196
Epoch 8/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1496 - loss: 2.6594 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1471 - loss: 2.6619
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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1475 - loss: 2.6535
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1483 - loss: 2.6499
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1489 - loss: 2.6465
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1496 - loss: 2.6432
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1501 - loss: 2.6412 - val_accuracy: 0.2172 - val_loss: 2.2969
Epoch 9/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1687 - loss: 2.5678 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1639 - loss: 2.5698
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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1626 - loss: 2.5663
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1624 - loss: 2.5656
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Epoch 10/110

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Epoch 11/110

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Epoch 12/110

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[1m 34/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1616 - loss: 2.4549 
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[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1687 - loss: 2.4500
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1695 - loss: 2.4477
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1702 - loss: 2.4460 - val_accuracy: 0.2315 - val_loss: 2.1654
Epoch 13/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1406 - loss: 2.5292
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1833 - loss: 2.4122 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1881 - loss: 2.3926
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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1882 - loss: 2.3870
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1875 - loss: 2.3870
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1873 - loss: 2.3868
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1871 - loss: 2.3868
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1869 - loss: 2.3869 - val_accuracy: 0.2461 - val_loss: 2.1104
Epoch 14/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1719 - loss: 2.3917
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1973 - loss: 2.3805 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1948 - loss: 2.3693
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1933 - loss: 2.3666
[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1926 - loss: 2.3647
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1926 - loss: 2.3628
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1928 - loss: 2.3613
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1933 - loss: 2.3596
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1936 - loss: 2.3583 - val_accuracy: 0.2383 - val_loss: 2.0801
Epoch 15/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1863 - loss: 2.3591 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1853 - loss: 2.3555
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1920 - loss: 2.3340 - val_accuracy: 0.2450 - val_loss: 2.0452
Epoch 16/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2344 - loss: 2.2343
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1935 - loss: 2.3080 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1937 - loss: 2.3082
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Epoch 17/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.1562 - loss: 2.2961
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1889 - loss: 2.2771 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1984 - loss: 2.2673
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2055 - loss: 2.2599
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2070 - loss: 2.2560
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2072 - loss: 2.2544
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2074 - loss: 2.2534 - val_accuracy: 0.2836 - val_loss: 1.9783
Epoch 18/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2031 - loss: 2.2874
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2105 - loss: 2.2400 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2099 - loss: 2.2328
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[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2090 - loss: 2.2284
[1m183/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2090 - loss: 2.2273
[1m217/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2095 - loss: 2.2262
[1m256/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2103 - loss: 2.2246
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.2110 - loss: 2.2237
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2110 - loss: 2.2237 - val_accuracy: 0.2834 - val_loss: 1.9543
Epoch 19/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2344 - loss: 2.3960
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2218 - loss: 2.2485 
[1m 70/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2225 - loss: 2.2355
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2218 - loss: 2.2279
[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2222 - loss: 2.2202
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2225 - loss: 2.2159
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2226 - loss: 2.2126
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2224 - loss: 2.2104
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2221 - loss: 2.2091 - val_accuracy: 0.2810 - val_loss: 1.9382
Epoch 20/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.1875 - loss: 2.2332
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2001 - loss: 2.2231 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2110 - loss: 2.2080
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[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2168 - loss: 2.1981
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2178 - loss: 2.1947
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2185 - loss: 2.1925
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2196 - loss: 2.1885 - val_accuracy: 0.2901 - val_loss: 1.9191
Epoch 21/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1719 - loss: 2.2954
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2141 - loss: 2.1534 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2192 - loss: 2.1587
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2213 - loss: 2.1577
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2226 - loss: 2.1565
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2236 - loss: 2.1561
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2242 - loss: 2.1557
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2245 - loss: 2.1557
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2248 - loss: 2.1556 - val_accuracy: 0.2986 - val_loss: 1.8947
Epoch 22/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1875 - loss: 2.1363
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2377 - loss: 2.1283 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2374 - loss: 2.1319
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[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2382 - loss: 2.1321
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2381 - loss: 2.1304
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2381 - loss: 2.1298
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2380 - loss: 2.1302
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2379 - loss: 2.1305 - val_accuracy: 0.3097 - val_loss: 1.8757
Epoch 23/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2031 - loss: 2.1499
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2417 - loss: 2.1154 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2395 - loss: 2.1182
[1m119/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2380 - loss: 2.1221
[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2370 - loss: 2.1245
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2365 - loss: 2.1255
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2361 - loss: 2.1257
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2359 - loss: 2.1255
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2358 - loss: 2.1252 - val_accuracy: 0.3088 - val_loss: 1.8615
Epoch 24/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2344 - loss: 2.0331
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2312 - loss: 2.0926 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2347 - loss: 2.0989
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2368 - loss: 2.0996
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2378 - loss: 2.1003
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2383 - loss: 2.1006
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2382 - loss: 2.1007
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2382 - loss: 2.1007
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2381 - loss: 2.1006 - val_accuracy: 0.3154 - val_loss: 1.8484
Epoch 25/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1875 - loss: 2.1417
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2264 - loss: 2.0909 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2312 - loss: 2.0936
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2331 - loss: 2.0932
[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2338 - loss: 2.0934
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2346 - loss: 2.0930
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2355 - loss: 2.0916
[1m259/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2360 - loss: 2.0907
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2365 - loss: 2.0900 - val_accuracy: 0.3234 - val_loss: 1.8346
Epoch 26/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2812 - loss: 1.9402
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2764 - loss: 2.0432 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2647 - loss: 2.0622
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[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2532 - loss: 2.0708
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Epoch 27/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2322 - loss: 2.0825 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2402 - loss: 2.0820
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[1m220/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2476 - loss: 2.0714
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Epoch 28/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2395 - loss: 2.0461 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2448 - loss: 2.0428
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[1m237/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2504 - loss: 2.0426
[1m276/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2509 - loss: 2.0429
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2511 - loss: 2.0430 - val_accuracy: 0.3345 - val_loss: 1.8086
Epoch 29/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2344 - loss: 2.0865
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2545 - loss: 2.0800 
[1m 70/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2594 - loss: 2.0624
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[1m145/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2602 - loss: 2.0546
[1m183/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2603 - loss: 2.0540
[1m219/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2604 - loss: 2.0527
[1m257/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2603 - loss: 2.0514
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.2605 - loss: 2.0500
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2605 - loss: 2.0499 - val_accuracy: 0.3389 - val_loss: 1.7994
Epoch 30/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2595 - loss: 2.0351 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2601 - loss: 2.0366
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2603 - loss: 2.0378
[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2611 - loss: 2.0370
[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2615 - loss: 2.0365
[1m222/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2616 - loss: 2.0361
[1m257/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2619 - loss: 2.0354
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2621 - loss: 2.0343 - val_accuracy: 0.3356 - val_loss: 1.7874
Epoch 31/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2969 - loss: 1.9931
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2723 - loss: 2.0164 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2722 - loss: 2.0203
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2708 - loss: 2.0217
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Epoch 32/110

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Epoch 33/110

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Epoch 34/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2810 - loss: 1.9840 
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[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2779 - loss: 1.9804
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2778 - loss: 1.9811
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2776 - loss: 1.9818
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2776 - loss: 1.9821 - val_accuracy: 0.3456 - val_loss: 1.7633
Epoch 35/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3064 - loss: 1.9273 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2935 - loss: 1.9478
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[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2829 - loss: 1.9676
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2824 - loss: 1.9683
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2822 - loss: 1.9686 - val_accuracy: 0.3421 - val_loss: 1.7468
Epoch 36/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3014 - loss: 1.9408 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2955 - loss: 1.9486
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2902 - loss: 1.9587
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2896 - loss: 1.9597
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Epoch 37/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2934 - loss: 1.9284 
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Epoch 38/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2706 - loss: 1.9551 
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Epoch 39/110

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Epoch 40/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3052 - loss: 1.9319 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3003 - loss: 1.9333
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[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2990 - loss: 1.9292
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2980 - loss: 1.9291
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2972 - loss: 1.9290
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2968 - loss: 1.9290 - val_accuracy: 0.3547 - val_loss: 1.7100
Epoch 41/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3043 - loss: 1.9309 
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3023 - loss: 1.9124
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3019 - loss: 1.9119
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Epoch 42/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3217 - loss: 1.9083 
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Epoch 43/110

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Epoch 44/110

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Epoch 45/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2976 - loss: 1.9096 
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3019 - loss: 1.8932
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3023 - loss: 1.8931
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3025 - loss: 1.8931 - val_accuracy: 0.3500 - val_loss: 1.6758
Epoch 46/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2812 - loss: 1.9218
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2947 - loss: 1.8755 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2984 - loss: 1.8715
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3037 - loss: 1.8776
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3048 - loss: 1.8788
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3055 - loss: 1.8801
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3060 - loss: 1.8810
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3066 - loss: 1.8815 - val_accuracy: 0.3643 - val_loss: 1.6652
Epoch 47/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3166 - loss: 1.8520 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3164 - loss: 1.8517
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3133 - loss: 1.8616
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3132 - loss: 1.8627
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Epoch 48/110

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Epoch 49/110

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Epoch 50/110

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Epoch 51/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2967 - loss: 1.8833 
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[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3169 - loss: 1.8671
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Epoch 52/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3301 - loss: 1.8088 
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[1m218/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3168 - loss: 1.8455
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Epoch 53/110

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Epoch 54/110

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Epoch 55/110

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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3265 - loss: 1.8404 - val_accuracy: 0.3652 - val_loss: 1.6431
Epoch 56/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3086 - loss: 1.8446 
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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3196 - loss: 1.8262
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3200 - loss: 1.8258
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3203 - loss: 1.8257 - val_accuracy: 0.3719 - val_loss: 1.6254
Epoch 57/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3138 - loss: 1.8297 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3157 - loss: 1.8338
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[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3192 - loss: 1.8310
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3206 - loss: 1.8310
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3220 - loss: 1.8306
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3230 - loss: 1.8304
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3235 - loss: 1.8302 - val_accuracy: 0.3708 - val_loss: 1.6266
Epoch 58/110

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[1m 34/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3057 - loss: 1.8627 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3119 - loss: 1.8459
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[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3224 - loss: 1.8232
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3235 - loss: 1.8211
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Epoch 59/110

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Epoch 60/110

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Epoch 61/110

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Epoch 62/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3316 - loss: 1.8116 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3371 - loss: 1.8076
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3365 - loss: 1.8050
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3363 - loss: 1.8050
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Epoch 63/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3323 - loss: 1.8009 
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Epoch 64/110

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Epoch 65/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3750 - loss: 1.7720
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3299 - loss: 1.7471 
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Epoch 66/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3906 - loss: 1.5933
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3521 - loss: 1.7125 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3484 - loss: 1.7286
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[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3429 - loss: 1.7527
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3422 - loss: 1.7564
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3406 - loss: 1.7624 - val_accuracy: 0.3717 - val_loss: 1.5998
Epoch 67/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2656 - loss: 1.8016
[1m 33/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3382 - loss: 1.7881 
[1m 68/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3411 - loss: 1.7868
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[1m143/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3406 - loss: 1.7840
[1m180/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3394 - loss: 1.7838
[1m220/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3387 - loss: 1.7838
[1m257/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3383 - loss: 1.7845
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3382 - loss: 1.7843 - val_accuracy: 0.3671 - val_loss: 1.6024
Epoch 68/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2500 - loss: 1.8612
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3215 - loss: 1.7918 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3299 - loss: 1.7864
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3332 - loss: 1.7822
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3346 - loss: 1.7815
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3359 - loss: 1.7818
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3373 - loss: 1.7812
[1m275/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3381 - loss: 1.7803
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3384 - loss: 1.7800 - val_accuracy: 0.3660 - val_loss: 1.6012
Epoch 69/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3125 - loss: 1.8607
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3306 - loss: 1.7875 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3336 - loss: 1.7823
[1m120/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3339 - loss: 1.7794
[1m160/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3351 - loss: 1.7772
[1m199/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3358 - loss: 1.7756
[1m239/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3364 - loss: 1.7748
[1m278/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3368 - loss: 1.7740
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Epoch 70/110

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Epoch 71/110

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Epoch 72/110

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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3464 - loss: 1.7694
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3457 - loss: 1.7700 - val_accuracy: 0.3639 - val_loss: 1.5938
Epoch 73/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3588 - loss: 1.6999 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3570 - loss: 1.7207
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[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3520 - loss: 1.7370
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3512 - loss: 1.7391
[1m263/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3506 - loss: 1.7411
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3502 - loss: 1.7426 - val_accuracy: 0.3767 - val_loss: 1.6087
Epoch 74/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3713 - loss: 1.7520 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3656 - loss: 1.7496
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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3574 - loss: 1.7530
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Epoch 75/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3529 - loss: 1.7536 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3486 - loss: 1.7572
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3467 - loss: 1.7609 - val_accuracy: 0.3747 - val_loss: 1.5919
Epoch 76/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.4688 - loss: 1.8760
[1m 34/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3692 - loss: 1.7846 
[1m 69/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3651 - loss: 1.7579
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[1m146/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3599 - loss: 1.7476
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[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3567 - loss: 1.7454
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3550 - loss: 1.7455 - val_accuracy: 0.3854 - val_loss: 1.5929
Epoch 77/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3594 - loss: 1.8874
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3586 - loss: 1.7460 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3518 - loss: 1.7486
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[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3500 - loss: 1.7568
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3496 - loss: 1.7569
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3498 - loss: 1.7560
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3499 - loss: 1.7553 - val_accuracy: 0.3758 - val_loss: 1.5952
Epoch 78/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2969 - loss: 1.8382
[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3699 - loss: 1.7126 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3657 - loss: 1.7163
[1m120/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3628 - loss: 1.7207
[1m161/292[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3604 - loss: 1.7231
[1m200/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3582 - loss: 1.7248
[1m240/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3565 - loss: 1.7268
[1m280/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3552 - loss: 1.7292
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3549 - loss: 1.7299 - val_accuracy: 0.3778 - val_loss: 1.5917
Epoch 79/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4531 - loss: 1.6397
[1m 32/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3644 - loss: 1.7226 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3648 - loss: 1.7259
[1m109/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3616 - loss: 1.7291
[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3588 - loss: 1.7316
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3565 - loss: 1.7350
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3550 - loss: 1.7367
[1m262/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3539 - loss: 1.7380
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3532 - loss: 1.7389 - val_accuracy: 0.3721 - val_loss: 1.5939
Epoch 80/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3750 - loss: 1.8062
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3577 - loss: 1.7315 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3553 - loss: 1.7299
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3538 - loss: 1.7314
[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3535 - loss: 1.7312
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3531 - loss: 1.7313
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3525 - loss: 1.7322
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3522 - loss: 1.7324
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Epoch 81/110

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Epoch 82/110

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Epoch 83/110

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Epoch 84/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3611 - loss: 1.7581 
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[1m290/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3519 - loss: 1.7301
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Epoch 85/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3540 - loss: 1.7100 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3504 - loss: 1.7212
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Epoch 86/110

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Epoch 87/110

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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3511 - loss: 1.7246
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Epoch 88/110

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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3580 - loss: 1.7174 - val_accuracy: 0.3771 - val_loss: 1.5796
Epoch 89/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3746 - loss: 1.6799 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3708 - loss: 1.6867
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[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3671 - loss: 1.6978
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3659 - loss: 1.7011
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3652 - loss: 1.7034
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3649 - loss: 1.7044 - val_accuracy: 0.3839 - val_loss: 1.5873
Epoch 90/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3552 - loss: 1.7190 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3568 - loss: 1.7106
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[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3556 - loss: 1.7092
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3556 - loss: 1.7100
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3555 - loss: 1.7107
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3555 - loss: 1.7113 - val_accuracy: 0.3900 - val_loss: 1.5793
Epoch 91/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3653 - loss: 1.7110 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3618 - loss: 1.7082
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[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3582 - loss: 1.7045
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3577 - loss: 1.7051
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3573 - loss: 1.7064 - val_accuracy: 0.3719 - val_loss: 1.5824

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:14[0m 873ms/step
[1m70/86[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 731us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
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Saved model to disk.

Accuracy capturado en la ejecución 20: 36.5 [%]
F1-score capturado en la ejecución 20: 36.17 [%]

=== EJECUCIÓN 21 ===

--- TRAIN (ejecución 21) ---

--- TEST (ejecución 21) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18676, 6, 250)

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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m75/86[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 679us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step  
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[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 64/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 795us/step
[1m134/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 758us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 37.19 [%]
Global F1 score (validation) = 37.24 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[2.2263582e-01 1.9404203e-01 2.1561058e-01 ... 1.0368678e-06
  1.4271738e-01 1.4390514e-02]
 [2.1270922e-01 2.0452806e-01 2.1307862e-01 ... 2.4835583e-06
  1.5600936e-01 1.0153877e-02]
 [1.7552947e-01 1.7165278e-01 1.8414260e-01 ... 1.9865562e-05
  2.2668089e-01 4.1564941e-02]
 ...
 [1.9315736e-01 2.0331211e-01 2.1588288e-01 ... 4.6713307e-05
  1.6339758e-01 3.6967441e-02]
 [1.8565679e-01 2.0400538e-01 2.0242633e-01 ... 9.7650445e-05
  1.7894825e-01 2.4821389e-02]
 [1.3337612e-01 1.5074210e-01 1.3337620e-01 ... 1.8020007e-03
  1.3465236e-01 1.4368395e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 44.72 [%]
Global accuracy score (test) = 35.07 [%]
Global F1 score (train) = 43.64 [%]
Global F1 score (test) = 34.81 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.15      0.23      0.18       184
 CAMINAR CON MÓVIL O LIBRO       0.21      0.24      0.22       184
       CAMINAR USUAL SPEED       0.20      0.27      0.23       184
            CAMINAR ZIGZAG       0.14      0.03      0.05       184
          DE PIE BARRIENDO       0.23      0.41      0.29       184
   DE PIE DOBLANDO TOALLAS       0.27      0.16      0.20       184
    DE PIE MOVIENDO LIBROS       0.30      0.40      0.34       184
          DE PIE USANDO PC       0.40      0.61      0.49       184
        FASE REPOSO CON K5       0.76      0.85      0.80       184
INCREMENTAL CICLOERGOMETRO       0.92      0.60      0.72       184
           SENTADO LEYENDO       0.36      0.30      0.33       184
         SENTADO USANDO PC       0.18      0.16      0.17       184
      SENTADO VIENDO LA TV       0.27      0.18      0.22       184
   SUBIR Y BAJAR ESCALERAS       0.29      0.15      0.20       184
                    TROTAR       0.84      0.73      0.78       161

                  accuracy                           0.35      2737
                 macro avg       0.37      0.35      0.35      2737
              weighted avg       0.36      0.35      0.34      2737

2025-10-28 14:24:46.533900: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 14:24:46.545238: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1761657886.558414 2379918 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761657886.562470 2379918 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761657886.572735 2379918 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657886.572756 2379918 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657886.572768 2379918 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657886.572769 2379918 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 14:24:46.576092: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1761657888.928232 2379918 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13733 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/110
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761657890.618539 2380044 service.cc:152] XLA service 0x7734ac00cdf0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761657890.618590 2380044 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 14:24:50.652605: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
I0000 00:00:1761657890.818916 2380044 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761657893.113791 2380044 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16:46[0m 3s/step - accuracy: 0.0469 - loss: 4.0836
[1m 33/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0653 - loss: 3.7073  
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0716 - loss: 3.6377
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Epoch 2/110

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Epoch 3/110

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Epoch 4/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1172 - loss: 2.9623 
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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1186 - loss: 2.9550
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1194 - loss: 2.9494
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1197 - loss: 2.9463 - val_accuracy: 0.1956 - val_loss: 2.3376
Epoch 5/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1413 - loss: 2.8505 
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[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1372 - loss: 2.8191
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1372 - loss: 2.8160
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Epoch 6/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1332 - loss: 2.7735 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1353 - loss: 2.7598
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1383 - loss: 2.7454 - val_accuracy: 0.2131 - val_loss: 2.2761
Epoch 7/110

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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1469 - loss: 2.6716 - val_accuracy: 0.2146 - val_loss: 2.2443
Epoch 8/110

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[1m 34/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1604 - loss: 2.6105 
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[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1543 - loss: 2.6231
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[1m222/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1545 - loss: 2.6213
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1554 - loss: 2.6189 - val_accuracy: 0.2207 - val_loss: 2.2176
Epoch 9/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2031 - loss: 2.6319
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1791 - loss: 2.5289 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1760 - loss: 2.5313
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[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1729 - loss: 2.5347
[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1722 - loss: 2.5352
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1717 - loss: 2.5358
[1m262/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1711 - loss: 2.5364
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1707 - loss: 2.5365 - val_accuracy: 0.2363 - val_loss: 2.1658
Epoch 10/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 25ms/step - accuracy: 0.1562 - loss: 2.3006
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1632 - loss: 2.4834 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1675 - loss: 2.4974
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1702 - loss: 2.4993
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1712 - loss: 2.4996
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1718 - loss: 2.4988
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1723 - loss: 2.4973
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1728 - loss: 2.4959
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1732 - loss: 2.4950 - val_accuracy: 0.2446 - val_loss: 2.1236
Epoch 11/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1406 - loss: 2.4353
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1770 - loss: 2.4253 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1767 - loss: 2.4288
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1765 - loss: 2.4307
[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1770 - loss: 2.4320
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1774 - loss: 2.4329
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1774 - loss: 2.4333
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1775 - loss: 2.4336
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1776 - loss: 2.4338 - val_accuracy: 0.2544 - val_loss: 2.0938
Epoch 12/110

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[1m 82/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1794 - loss: 2.4136
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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1845 - loss: 2.4030
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Epoch 13/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2031 - loss: 2.3776
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1996 - loss: 2.3633 
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Epoch 14/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2500 - loss: 2.2737
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2050 - loss: 2.3224 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2048 - loss: 2.3177
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[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2018 - loss: 2.3174
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2006 - loss: 2.3185
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2001 - loss: 2.3192
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1997 - loss: 2.3193 - val_accuracy: 0.2727 - val_loss: 2.0013
Epoch 15/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1562 - loss: 2.2446
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1860 - loss: 2.2719 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1930 - loss: 2.2737
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1950 - loss: 2.2765
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1964 - loss: 2.2786
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1975 - loss: 2.2807
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1983 - loss: 2.2813
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1992 - loss: 2.2811
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1997 - loss: 2.2807 - val_accuracy: 0.2764 - val_loss: 1.9739
Epoch 16/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2656 - loss: 2.1525
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2325 - loss: 2.2254 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2227 - loss: 2.2372
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2197 - loss: 2.2444
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2189 - loss: 2.2456
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2182 - loss: 2.2458
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2175 - loss: 2.2460
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2168 - loss: 2.2460
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2166 - loss: 2.2459 - val_accuracy: 0.2960 - val_loss: 1.9543
Epoch 17/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1406 - loss: 2.3162
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2023 - loss: 2.2158 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2094 - loss: 2.2121
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[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2113 - loss: 2.2153
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[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2118 - loss: 2.2167
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2121 - loss: 2.2166 - val_accuracy: 0.2927 - val_loss: 1.9178
Epoch 18/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 29ms/step - accuracy: 0.1562 - loss: 2.3697
[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2226 - loss: 2.2138 
[1m 83/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2247 - loss: 2.2014
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[1m161/292[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2231 - loss: 2.1982
[1m198/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2222 - loss: 2.1976
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2216 - loss: 2.1969
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2208 - loss: 2.1960 - val_accuracy: 0.2947 - val_loss: 1.9098
Epoch 19/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2344 - loss: 2.0745
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[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2342 - loss: 2.1513
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[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2306 - loss: 2.1614
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2295 - loss: 2.1635
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2286 - loss: 2.1655
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2283 - loss: 2.1662 - val_accuracy: 0.2995 - val_loss: 1.9005
Epoch 20/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.1875 - loss: 2.2899
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2405 - loss: 2.1556 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2378 - loss: 2.1516
[1m119/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2365 - loss: 2.1515
[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2366 - loss: 2.1494
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2365 - loss: 2.1483
[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2367 - loss: 2.1470
[1m277/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2366 - loss: 2.1466
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2365 - loss: 2.1465 - val_accuracy: 0.3023 - val_loss: 1.8773
Epoch 21/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.2188 - loss: 2.0583
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2227 - loss: 2.1271 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2249 - loss: 2.1352
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2264 - loss: 2.1365
[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2268 - loss: 2.1371
[1m197/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2269 - loss: 2.1372
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2271 - loss: 2.1377
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2276 - loss: 2.1374
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2278 - loss: 2.1372 - val_accuracy: 0.2999 - val_loss: 1.8658
Epoch 22/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2656 - loss: 2.2031
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2567 - loss: 2.1177 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2536 - loss: 2.1041
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2493 - loss: 2.1042
[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2476 - loss: 2.1049
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2458 - loss: 2.1047
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2445 - loss: 2.1050
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2433 - loss: 2.1055
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2426 - loss: 2.1058 - val_accuracy: 0.3147 - val_loss: 1.8628
Epoch 23/110

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[1m 32/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2250 - loss: 2.0849 
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[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2389 - loss: 2.0911
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Epoch 24/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2400 - loss: 2.0913 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2444 - loss: 2.0889
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[1m216/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2453 - loss: 2.0925
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Epoch 25/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2188 - loss: 1.9671
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2644 - loss: 2.0449 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2593 - loss: 2.0546
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[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2530 - loss: 2.0645
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2522 - loss: 2.0658
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2515 - loss: 2.0669
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2513 - loss: 2.0672 - val_accuracy: 0.3179 - val_loss: 1.8341
Epoch 26/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.3281 - loss: 1.8853
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2584 - loss: 2.0359 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2533 - loss: 2.0509
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2532 - loss: 2.0540
[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2538 - loss: 2.0549
[1m198/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2545 - loss: 2.0539
[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2547 - loss: 2.0533
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2547 - loss: 2.0532
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2547 - loss: 2.0533 - val_accuracy: 0.3338 - val_loss: 1.8205
Epoch 27/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.3281 - loss: 2.0553
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2653 - loss: 2.0507 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2601 - loss: 2.0515
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2579 - loss: 2.0499
[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2571 - loss: 2.0484
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2567 - loss: 2.0469
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2566 - loss: 2.0466
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2564 - loss: 2.0461
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2563 - loss: 2.0459 - val_accuracy: 0.3314 - val_loss: 1.8064
Epoch 28/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1875 - loss: 2.1011
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2399 - loss: 2.0401 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2449 - loss: 2.0392
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2468 - loss: 2.0374
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[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2500 - loss: 2.0341
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2511 - loss: 2.0335
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2520 - loss: 2.0333
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2525 - loss: 2.0329 - val_accuracy: 0.3432 - val_loss: 1.7940
Epoch 29/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.2031 - loss: 2.2905
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2440 - loss: 2.0612 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2529 - loss: 2.0378
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2556 - loss: 2.0311
[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2569 - loss: 2.0283
[1m199/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2577 - loss: 2.0264
[1m237/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2583 - loss: 2.0252
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2587 - loss: 2.0241
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2589 - loss: 2.0236 - val_accuracy: 0.3301 - val_loss: 1.7930
Epoch 30/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.3438 - loss: 1.9075
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2850 - loss: 1.9954 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2776 - loss: 2.0055
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2750 - loss: 2.0091
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2727 - loss: 2.0106
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2715 - loss: 2.0107
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2704 - loss: 2.0111
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2699 - loss: 2.0108
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2698 - loss: 2.0108 - val_accuracy: 0.3419 - val_loss: 1.7895
Epoch 31/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2656 - loss: 1.9816
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2720 - loss: 1.9821 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2720 - loss: 1.9956
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2723 - loss: 1.9992
[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2732 - loss: 1.9987
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2739 - loss: 1.9975
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2744 - loss: 1.9964
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2745 - loss: 1.9958
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2745 - loss: 1.9955 - val_accuracy: 0.3441 - val_loss: 1.7817
Epoch 32/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3906 - loss: 1.8561
[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3015 - loss: 1.9764 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2931 - loss: 1.9841
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2887 - loss: 1.9892
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2858 - loss: 1.9915
[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2843 - loss: 1.9920
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2831 - loss: 1.9922
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2823 - loss: 1.9921
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2816 - loss: 1.9921 - val_accuracy: 0.3401 - val_loss: 1.7698
Epoch 33/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3438 - loss: 1.8510
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2718 - loss: 2.0032 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2768 - loss: 1.9943
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2775 - loss: 1.9905
[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2776 - loss: 1.9876
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2780 - loss: 1.9848
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2781 - loss: 1.9832
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2778 - loss: 1.9828
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2777 - loss: 1.9827 - val_accuracy: 0.3382 - val_loss: 1.7750
Epoch 34/110

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Epoch 35/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2654 - loss: 1.9840 
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Epoch 36/110

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Epoch 37/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2944 - loss: 1.9518 
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2914 - loss: 1.9455
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2913 - loss: 1.9451
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Epoch 38/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2795 - loss: 1.9541 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2766 - loss: 1.9536
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2799 - loss: 1.9498
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2809 - loss: 1.9490
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Epoch 39/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3014 - loss: 1.9116 
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Epoch 40/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2848 - loss: 1.9382 
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Epoch 41/110

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Epoch 42/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3061 - loss: 1.9122 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3025 - loss: 1.9106
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3010 - loss: 1.9119
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3012 - loss: 1.9116
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3014 - loss: 1.9113 - val_accuracy: 0.3523 - val_loss: 1.7046
Epoch 43/110

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[1m 31/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3095 - loss: 1.9245 
[1m 70/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3056 - loss: 1.9162
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[1m182/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3036 - loss: 1.9108
[1m222/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3039 - loss: 1.9100
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3043 - loss: 1.9096
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3044 - loss: 1.9097 - val_accuracy: 0.3628 - val_loss: 1.6994
Epoch 44/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2955 - loss: 1.8745 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2945 - loss: 1.8944
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[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2964 - loss: 1.8994
[1m279/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2968 - loss: 1.8998
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Epoch 45/110

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Epoch 46/110

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Epoch 47/110

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Epoch 48/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3010 - loss: 1.8878 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3015 - loss: 1.8861
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[1m160/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3036 - loss: 1.8853
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[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3049 - loss: 1.8822
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3057 - loss: 1.8799 - val_accuracy: 0.3560 - val_loss: 1.6750
Epoch 49/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3184 - loss: 1.8391 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3192 - loss: 1.8437
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[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3154 - loss: 1.8543
[1m237/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3143 - loss: 1.8567
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3136 - loss: 1.8582
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Epoch 50/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3204 - loss: 1.8811 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3207 - loss: 1.8775
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Epoch 51/110

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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3123 - loss: 1.8622
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Epoch 52/110

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Epoch 53/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3238 - loss: 1.7945 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3246 - loss: 1.8167
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3208 - loss: 1.8387
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3206 - loss: 1.8395
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3206 - loss: 1.8398 - val_accuracy: 0.3558 - val_loss: 1.6582
Epoch 54/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3438 - loss: 1.8136
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3243 - loss: 1.8213 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3214 - loss: 1.8256
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[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3205 - loss: 1.8227
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3198 - loss: 1.8244
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3194 - loss: 1.8268
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3194 - loss: 1.8279
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3194 - loss: 1.8284 - val_accuracy: 0.3536 - val_loss: 1.6621
Epoch 55/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3438 - loss: 1.9420
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3225 - loss: 1.8453 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3256 - loss: 1.8391
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[1m237/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3237 - loss: 1.8384
[1m276/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3236 - loss: 1.8385
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Epoch 56/110

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Epoch 57/110

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Epoch 58/110

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Epoch 59/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3193 - loss: 1.8761 
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[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3251 - loss: 1.8358
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Epoch 60/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3171 - loss: 1.8300 
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Epoch 61/110

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Epoch 62/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3194 - loss: 1.7928 
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Epoch 63/110

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[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3317 - loss: 1.8053
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3310 - loss: 1.8046 - val_accuracy: 0.3636 - val_loss: 1.6277
Epoch 64/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3429 - loss: 1.7727 
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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3342 - loss: 1.7899
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3331 - loss: 1.7919
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3330 - loss: 1.7921
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3330 - loss: 1.7923 - val_accuracy: 0.3656 - val_loss: 1.6279
Epoch 65/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.2344 - loss: 1.7628
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2933 - loss: 1.8121 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3068 - loss: 1.8080
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3132 - loss: 1.8061
[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3178 - loss: 1.8031
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3208 - loss: 1.8003
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3227 - loss: 1.7991
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3242 - loss: 1.7985
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3247 - loss: 1.7982 - val_accuracy: 0.3634 - val_loss: 1.6253
Epoch 66/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2500 - loss: 1.7890
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3223 - loss: 1.7722 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3201 - loss: 1.7810
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3200 - loss: 1.7853
[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3209 - loss: 1.7879
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3224 - loss: 1.7905
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3235 - loss: 1.7910
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Epoch 67/110

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Epoch 68/110

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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3286 - loss: 1.7769
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Epoch 69/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3565 - loss: 1.7622 
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[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3479 - loss: 1.7689
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3467 - loss: 1.7702
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3459 - loss: 1.7715
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3455 - loss: 1.7720 - val_accuracy: 0.3663 - val_loss: 1.6199
Epoch 70/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3300 - loss: 1.8009 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3280 - loss: 1.7938
[1m119/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3292 - loss: 1.7872
[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3307 - loss: 1.7846
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3320 - loss: 1.7826
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3333 - loss: 1.7807
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3341 - loss: 1.7790
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Epoch 71/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3526 - loss: 1.7385 
[1m 82/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3469 - loss: 1.7525
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3470 - loss: 1.7630
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3468 - loss: 1.7640
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Epoch 72/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3483 - loss: 1.7783 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3433 - loss: 1.7780
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[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3392 - loss: 1.7722
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3391 - loss: 1.7708
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3392 - loss: 1.7708 - val_accuracy: 0.3711 - val_loss: 1.6108
Epoch 73/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2656 - loss: 1.8855
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3396 - loss: 1.7670 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3408 - loss: 1.7631
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[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3410 - loss: 1.7667
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3410 - loss: 1.7664 - val_accuracy: 0.3623 - val_loss: 1.6136
Epoch 74/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.3281 - loss: 1.6327
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3522 - loss: 1.7536 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3488 - loss: 1.7694
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[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3459 - loss: 1.7718
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3456 - loss: 1.7694
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3456 - loss: 1.7678
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3455 - loss: 1.7667
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3455 - loss: 1.7662 - val_accuracy: 0.3774 - val_loss: 1.6223
Epoch 75/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3906 - loss: 1.4925
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3314 - loss: 1.7808 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3376 - loss: 1.7708
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3403 - loss: 1.7635
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3423 - loss: 1.7586
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3427 - loss: 1.7564
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3428 - loss: 1.7553
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3432 - loss: 1.7555
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3433 - loss: 1.7556 - val_accuracy: 0.3600 - val_loss: 1.6137
Epoch 76/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2500 - loss: 1.7787
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3424 - loss: 1.7532 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3458 - loss: 1.7537
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3456 - loss: 1.7538
[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3467 - loss: 1.7517
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3472 - loss: 1.7504
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3477 - loss: 1.7495
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3478 - loss: 1.7498
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3477 - loss: 1.7501 - val_accuracy: 0.3811 - val_loss: 1.6135
Epoch 77/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3281 - loss: 1.6759
[1m 33/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3387 - loss: 1.7475 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3446 - loss: 1.7457
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3458 - loss: 1.7479
[1m143/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3466 - loss: 1.7488
[1m179/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3472 - loss: 1.7498
[1m219/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3473 - loss: 1.7509
[1m258/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3473 - loss: 1.7517
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Epoch 78/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3299 - loss: 1.7316 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3364 - loss: 1.7320
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Epoch 79/110

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[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3398 - loss: 1.7651
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Epoch 80/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3387 - loss: 1.7725 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3426 - loss: 1.7578
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[1m197/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3464 - loss: 1.7464
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3467 - loss: 1.7451
[1m275/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3468 - loss: 1.7445
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3469 - loss: 1.7445 - val_accuracy: 0.3741 - val_loss: 1.6168
Epoch 81/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3485 - loss: 1.7200 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3476 - loss: 1.7322
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[1m200/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3447 - loss: 1.7390
[1m242/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3443 - loss: 1.7394
[1m281/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3442 - loss: 1.7393
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3442 - loss: 1.7394 - val_accuracy: 0.3602 - val_loss: 1.6036
Epoch 82/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3425 - loss: 1.7128 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3442 - loss: 1.7148
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[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3445 - loss: 1.7299
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3445 - loss: 1.7322
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Epoch 83/110

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[1m 42/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3179 - loss: 1.7640 
[1m 82/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3297 - loss: 1.7487
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Epoch 84/110

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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3524 - loss: 1.7301
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Epoch 85/110

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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3516 - loss: 1.7371 - val_accuracy: 0.3648 - val_loss: 1.6015
Epoch 86/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4062 - loss: 1.7628
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3420 - loss: 1.7645 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3427 - loss: 1.7502
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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3472 - loss: 1.7387
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3475 - loss: 1.7377
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3477 - loss: 1.7371 - val_accuracy: 0.3728 - val_loss: 1.6151
Epoch 87/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3352 - loss: 1.7373 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3431 - loss: 1.7310
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3480 - loss: 1.7270
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3486 - loss: 1.7287
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3492 - loss: 1.7292
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3496 - loss: 1.7296
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3498 - loss: 1.7298 - val_accuracy: 0.3795 - val_loss: 1.6005
Epoch 88/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3805 - loss: 1.7089 
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3639 - loss: 1.7288
[1m275/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3626 - loss: 1.7286
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3620 - loss: 1.7285 - val_accuracy: 0.3789 - val_loss: 1.5955
Epoch 89/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3438 - loss: 1.6521
[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3551 - loss: 1.6869 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3584 - loss: 1.6867
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3584 - loss: 1.6927
[1m147/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3587 - loss: 1.6954
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3590 - loss: 1.6976
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3586 - loss: 1.7004
[1m262/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3579 - loss: 1.7032
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3573 - loss: 1.7050 - val_accuracy: 0.3708 - val_loss: 1.5959
Epoch 90/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.2656 - loss: 1.7365
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3249 - loss: 1.7274 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3351 - loss: 1.7258
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3383 - loss: 1.7308
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3403 - loss: 1.7319
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3420 - loss: 1.7315
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3434 - loss: 1.7306
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3445 - loss: 1.7296
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3450 - loss: 1.7292 - val_accuracy: 0.3774 - val_loss: 1.5959
Epoch 91/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3281 - loss: 1.6899
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3558 - loss: 1.7021 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3585 - loss: 1.7071
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3569 - loss: 1.7151
[1m147/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3557 - loss: 1.7211
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3554 - loss: 1.7238
[1m218/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3557 - loss: 1.7243
[1m258/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3564 - loss: 1.7236
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3567 - loss: 1.7235 - val_accuracy: 0.3824 - val_loss: 1.6048
Epoch 92/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2969 - loss: 1.7332
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3627 - loss: 1.7008 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3627 - loss: 1.7091
[1m107/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3622 - loss: 1.7130
[1m141/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3614 - loss: 1.7143
[1m182/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3605 - loss: 1.7149
[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3603 - loss: 1.7147
[1m259/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3598 - loss: 1.7151
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3594 - loss: 1.7155 - val_accuracy: 0.3769 - val_loss: 1.5977
Epoch 93/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3281 - loss: 1.5872
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3702 - loss: 1.6624 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3637 - loss: 1.6790
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3629 - loss: 1.6884
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3625 - loss: 1.6934
[1m182/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3620 - loss: 1.6952
[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3616 - loss: 1.6965
[1m254/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3615 - loss: 1.6974
[1m290/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3612 - loss: 1.6989
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3612 - loss: 1.6990 - val_accuracy: 0.3708 - val_loss: 1.5994

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:10[0m 830ms/step
[1m64/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 806us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 9ms/step
Saved model to disk.

Accuracy capturado en la ejecución 21: 35.07 [%]
F1-score capturado en la ejecución 21: 34.81 [%]

=== EJECUCIÓN 22 ===

--- TRAIN (ejecución 22) ---

--- TEST (ejecución 22) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18676, 6, 250)

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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
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[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step  
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[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 64/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 802us/step
[1m133/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 766us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 37.08 [%]
Global F1 score (validation) = 37.05 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[2.27912471e-01 2.01881513e-01 2.07366124e-01 ... 1.27098861e-06
  1.38956994e-01 7.31139351e-03]
 [2.28835061e-01 1.80899948e-01 2.06444740e-01 ... 1.49748644e-06
  1.43610522e-01 2.15992890e-02]
 [2.01887384e-01 1.63265362e-01 1.82387963e-01 ... 4.29286683e-06
  1.77061632e-01 7.36375898e-02]
 ...
 [2.13471100e-01 1.95802167e-01 2.06682712e-01 ... 1.22396887e-05
  1.55356869e-01 1.72080602e-02]
 [1.95280835e-01 2.12192416e-01 2.02944979e-01 ... 3.67777211e-05
  1.69594049e-01 1.04448842e-02]
 [1.11009404e-01 1.56383425e-01 1.28822535e-01 ... 1.38367910e-03
  1.21648461e-01 3.50173377e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 45.22 [%]
Global accuracy score (test) = 36.76 [%]
Global F1 score (train) = 44.07 [%]
Global F1 score (test) = 36.36 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.27      0.45      0.33       184
 CAMINAR CON MÓVIL O LIBRO       0.18      0.23      0.20       184
       CAMINAR USUAL SPEED       0.25      0.17      0.20       184
            CAMINAR ZIGZAG       0.14      0.09      0.11       184
          DE PIE BARRIENDO       0.22      0.38      0.28       184
   DE PIE DOBLANDO TOALLAS       0.29      0.18      0.23       184
    DE PIE MOVIENDO LIBROS       0.32      0.32      0.32       184
          DE PIE USANDO PC       0.42      0.54      0.48       184
        FASE REPOSO CON K5       0.57      0.82      0.67       184
INCREMENTAL CICLOERGOMETRO       0.87      0.62      0.72       184
           SENTADO LEYENDO       0.38      0.48      0.42       184
         SENTADO USANDO PC       0.20      0.14      0.17       184
      SENTADO VIENDO LA TV       0.38      0.21      0.27       184
   SUBIR Y BAJAR ESCALERAS       0.37      0.22      0.27       184
                    TROTAR       0.88      0.71      0.79       161

                  accuracy                           0.37      2737
                 macro avg       0.38      0.37      0.36      2737
              weighted avg       0.38      0.37      0.36      2737

2025-10-28 14:26:01.026425: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 14:26:01.037746: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1761657961.050853 2389609 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761657961.055084 2389609 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761657961.064928 2389609 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657961.064949 2389609 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657961.064951 2389609 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761657961.064953 2389609 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 14:26:01.068237: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1761657963.420607 2389609 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13733 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/110
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761657965.107867 2389708 service.cc:152] XLA service 0x7cfac000bb60 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761657965.107933 2389708 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 14:26:05.144584: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
I0000 00:00:1761657965.314874 2389708 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761657967.590458 2389708 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16:42[0m 3s/step - accuracy: 0.0781 - loss: 3.4093
[1m 34/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0692 - loss: 3.5788  
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0715 - loss: 3.5724
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[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0757 - loss: 3.5409
[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0770 - loss: 3.5281
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0781 - loss: 3.5152
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0791 - loss: 3.5028
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step - accuracy: 0.0799 - loss: 3.4933
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 16ms/step - accuracy: 0.0799 - loss: 3.4930 - val_accuracy: 0.1704 - val_loss: 2.4859
Epoch 2/110

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Epoch 3/110

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Epoch 4/110

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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1199 - loss: 2.9506 - val_accuracy: 0.1939 - val_loss: 2.3660
Epoch 5/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1330 - loss: 2.8325 
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1343 - loss: 2.8372
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1343 - loss: 2.8350
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1343 - loss: 2.8338 - val_accuracy: 0.1969 - val_loss: 2.3352
Epoch 6/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1593 - loss: 2.7209 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1505 - loss: 2.7404
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1480 - loss: 2.7473
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[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1459 - loss: 2.7509
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Epoch 7/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1455 - loss: 2.7436 
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Epoch 8/110

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Epoch 9/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1754 - loss: 2.5380 
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1681 - loss: 2.5465
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1675 - loss: 2.5467 - val_accuracy: 0.2444 - val_loss: 2.1936
Epoch 10/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1665 - loss: 2.5153 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1698 - loss: 2.5102
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[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1698 - loss: 2.5119
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1699 - loss: 2.5109
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1701 - loss: 2.5100
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1702 - loss: 2.5094 - val_accuracy: 0.2429 - val_loss: 2.1838
Epoch 11/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2188 - loss: 2.4325
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1811 - loss: 2.4737 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1751 - loss: 2.4773
[1m111/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1732 - loss: 2.4764
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1726 - loss: 2.4755
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1727 - loss: 2.4734
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1727 - loss: 2.4719
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1729 - loss: 2.4699
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1730 - loss: 2.4690 - val_accuracy: 0.2429 - val_loss: 2.1318
Epoch 12/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2344 - loss: 2.2800
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1973 - loss: 2.4176 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1971 - loss: 2.4191
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[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1940 - loss: 2.4201
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1926 - loss: 2.4199
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1914 - loss: 2.4200
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1906 - loss: 2.4198
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Epoch 13/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1813 - loss: 2.4119 
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Epoch 14/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2006 - loss: 2.3433 
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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1982 - loss: 2.3337
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1990 - loss: 2.3323 - val_accuracy: 0.2588 - val_loss: 2.0452
Epoch 15/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2103 - loss: 2.2892 
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[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2089 - loss: 2.2973
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2082 - loss: 2.2984
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2077 - loss: 2.2989
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2075 - loss: 2.2990 - val_accuracy: 0.2579 - val_loss: 2.0026
Epoch 16/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2183 - loss: 2.2564 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2109 - loss: 2.2650
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2096 - loss: 2.2667
[1m147/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2095 - loss: 2.2676
[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2093 - loss: 2.2685
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2094 - loss: 2.2682
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2095 - loss: 2.2673
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2095 - loss: 2.2667 - val_accuracy: 0.2666 - val_loss: 1.9719
Epoch 17/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2097 - loss: 2.2522 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2110 - loss: 2.2485
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[1m147/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2126 - loss: 2.2405
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2129 - loss: 2.2391
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2131 - loss: 2.2383
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2131 - loss: 2.2378
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2131 - loss: 2.2377 - val_accuracy: 0.2781 - val_loss: 1.9527
Epoch 18/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2161 - loss: 2.2334 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2130 - loss: 2.2245
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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2133 - loss: 2.2187
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2140 - loss: 2.2177
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2144 - loss: 2.2171 - val_accuracy: 0.2705 - val_loss: 1.9443
Epoch 19/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2812 - loss: 2.1702
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2433 - loss: 2.1786 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2325 - loss: 2.1849
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2290 - loss: 2.1842
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2272 - loss: 2.1838
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2253 - loss: 2.1846
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2243 - loss: 2.1855
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2234 - loss: 2.1867 - val_accuracy: 0.2958 - val_loss: 1.9160
Epoch 20/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1875 - loss: 2.2705
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2391 - loss: 2.1831 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2353 - loss: 2.1817
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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2309 - loss: 2.1803
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2298 - loss: 2.1798
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2291 - loss: 2.1796
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2288 - loss: 2.1790
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2287 - loss: 2.1780 - val_accuracy: 0.2858 - val_loss: 1.8998
Epoch 21/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.1875 - loss: 2.3121
[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2384 - loss: 2.1589 
[1m 82/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2388 - loss: 2.1546
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[1m161/292[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2352 - loss: 2.1528
[1m201/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2340 - loss: 2.1517
[1m241/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2331 - loss: 2.1510
[1m281/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2324 - loss: 2.1502
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2322 - loss: 2.1499 - val_accuracy: 0.2879 - val_loss: 1.8896
Epoch 22/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.0938 - loss: 2.4563
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2136 - loss: 2.1733 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2260 - loss: 2.1444
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2296 - loss: 2.1360
[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2314 - loss: 2.1324
[1m197/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2319 - loss: 2.1313
[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2321 - loss: 2.1312
[1m275/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2322 - loss: 2.1314
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2323 - loss: 2.1314 - val_accuracy: 0.3005 - val_loss: 1.8678
Epoch 23/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1719 - loss: 2.0586
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2177 - loss: 2.0995 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2245 - loss: 2.1070
[1m120/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2286 - loss: 2.1090
[1m160/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2314 - loss: 2.1089
[1m198/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2332 - loss: 2.1089
[1m237/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2344 - loss: 2.1086
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2351 - loss: 2.1082
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2354 - loss: 2.1080 - val_accuracy: 0.2979 - val_loss: 1.8567
Epoch 24/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2407 - loss: 2.1244 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2432 - loss: 2.1125
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[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2451 - loss: 2.1040
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2460 - loss: 2.0970 - val_accuracy: 0.3019 - val_loss: 1.8426
Epoch 25/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2500 - loss: 1.8729
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2464 - loss: 2.0667 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2517 - loss: 2.0661
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2480 - loss: 2.0728
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2476 - loss: 2.0745 - val_accuracy: 0.3092 - val_loss: 1.8332
Epoch 26/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2437 - loss: 2.0719 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2493 - loss: 2.0694
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2507 - loss: 2.0679
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2507 - loss: 2.0663
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2504 - loss: 2.0656
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2501 - loss: 2.0659
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2497 - loss: 2.0664
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2495 - loss: 2.0668 - val_accuracy: 0.3053 - val_loss: 1.8292
Epoch 27/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2565 - loss: 2.0482 
[1m 67/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2535 - loss: 2.0537
[1m105/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2520 - loss: 2.0592
[1m145/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2524 - loss: 2.0597
[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2530 - loss: 2.0586
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2534 - loss: 2.0580
[1m263/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2537 - loss: 2.0573
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2538 - loss: 2.0569 - val_accuracy: 0.3056 - val_loss: 1.8223
Epoch 28/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1719 - loss: 2.0101
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2543 - loss: 2.0125 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2542 - loss: 2.0234
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2539 - loss: 2.0289
[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2538 - loss: 2.0325
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2541 - loss: 2.0342
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2542 - loss: 2.0354
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2542 - loss: 2.0366
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2542 - loss: 2.0372 - val_accuracy: 0.3036 - val_loss: 1.8069
Epoch 29/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 25ms/step - accuracy: 0.2188 - loss: 2.0730
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2611 - loss: 2.0502 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2623 - loss: 2.0391
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Epoch 30/110

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Epoch 31/110

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Epoch 32/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2859 - loss: 1.9802 
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2716 - loss: 2.0042
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2714 - loss: 2.0044
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2714 - loss: 2.0042 - val_accuracy: 0.3286 - val_loss: 1.7738
Epoch 33/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2891 - loss: 1.9461 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2798 - loss: 1.9735
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[1m146/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2772 - loss: 1.9823
[1m182/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2767 - loss: 1.9846
[1m222/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2763 - loss: 1.9865
[1m262/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2758 - loss: 1.9877
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2754 - loss: 1.9881 - val_accuracy: 0.3340 - val_loss: 1.7585
Epoch 34/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2685 - loss: 2.0351 
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[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2811 - loss: 1.9952
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Epoch 35/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2560 - loss: 2.0359 
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Epoch 36/110

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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2800 - loss: 1.9724
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Epoch 37/110

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[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2721 - loss: 1.9666
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Epoch 38/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2964 - loss: 1.9192 
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[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2874 - loss: 1.9468
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2875 - loss: 1.9481
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Epoch 39/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2833 - loss: 1.9491 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2850 - loss: 1.9571
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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2867 - loss: 1.9558
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2871 - loss: 1.9544
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Epoch 40/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2842 - loss: 1.9502 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2863 - loss: 1.9517
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Epoch 41/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3028 - loss: 1.9319 
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Epoch 42/110

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Epoch 43/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2969 - loss: 1.8589
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3027 - loss: 1.9046 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2981 - loss: 1.9159
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[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2995 - loss: 1.9156
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2997 - loss: 1.9147 - val_accuracy: 0.3395 - val_loss: 1.6932
Epoch 44/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3125 - loss: 1.9109
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3172 - loss: 1.9000 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3101 - loss: 1.9023
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[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3065 - loss: 1.9047
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3055 - loss: 1.9060
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3047 - loss: 1.9070
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3040 - loss: 1.9075
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3037 - loss: 1.9075 - val_accuracy: 0.3502 - val_loss: 1.6817
Epoch 45/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3159 - loss: 1.8603 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3129 - loss: 1.8648
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[1m237/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3064 - loss: 1.8806
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Epoch 46/110

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Epoch 47/110

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Epoch 48/110

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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3106 - loss: 1.8728 - val_accuracy: 0.3454 - val_loss: 1.6723
Epoch 49/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3183 - loss: 1.9067 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3148 - loss: 1.8923
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3143 - loss: 1.8850
[1m147/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3140 - loss: 1.8817
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3135 - loss: 1.8810
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3132 - loss: 1.8801
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3130 - loss: 1.8798
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3127 - loss: 1.8796 - val_accuracy: 0.3508 - val_loss: 1.6565
Epoch 50/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3102 - loss: 1.8722 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3109 - loss: 1.8701
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[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3110 - loss: 1.8696
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3113 - loss: 1.8680
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3117 - loss: 1.8666
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3118 - loss: 1.8661 - val_accuracy: 0.3365 - val_loss: 1.6557
Epoch 51/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3021 - loss: 1.8869 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3008 - loss: 1.8770
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Epoch 52/110

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Epoch 53/110

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Epoch 54/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3027 - loss: 1.8443 
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[1m180/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3173 - loss: 1.8469
[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3180 - loss: 1.8465
[1m255/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3186 - loss: 1.8459
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3192 - loss: 1.8453 - val_accuracy: 0.3489 - val_loss: 1.6405
Epoch 55/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3170 - loss: 1.8323 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3163 - loss: 1.8356
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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3168 - loss: 1.8372
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3174 - loss: 1.8371
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3176 - loss: 1.8372 - val_accuracy: 0.3589 - val_loss: 1.6333
Epoch 56/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3394 - loss: 1.8113 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3316 - loss: 1.8211
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[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3236 - loss: 1.8326
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Epoch 57/110

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Epoch 58/110

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Epoch 59/110

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Epoch 60/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3320 - loss: 1.7995 
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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3241 - loss: 1.8165
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Epoch 61/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3387 - loss: 1.7945 
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3382 - loss: 1.8053
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Epoch 62/110

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Epoch 63/110

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Epoch 64/110

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Epoch 65/110

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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3375 - loss: 1.7872
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3372 - loss: 1.7893 - val_accuracy: 0.3689 - val_loss: 1.6060
Epoch 66/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3302 - loss: 1.7941 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3313 - loss: 1.7949
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[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3308 - loss: 1.7943
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3306 - loss: 1.7949
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3306 - loss: 1.7951
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3307 - loss: 1.7952 - val_accuracy: 0.3630 - val_loss: 1.6109
Epoch 67/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3317 - loss: 1.7830 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3351 - loss: 1.7888
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Epoch 68/110

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Epoch 69/110

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Epoch 70/110

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[1m289/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3359 - loss: 1.7748
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Epoch 71/110

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[1m 34/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3457 - loss: 1.7942 
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3395 - loss: 1.7759
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3399 - loss: 1.7750
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Epoch 72/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3452 - loss: 1.7970 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3465 - loss: 1.7783
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3442 - loss: 1.7716
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[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3379 - loss: 1.7754
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Epoch 73/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3278 - loss: 1.7783 
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[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3377 - loss: 1.7662
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3376 - loss: 1.7673
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3372 - loss: 1.7683 - val_accuracy: 0.3578 - val_loss: 1.5976
Epoch 74/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3750 - loss: 1.9296
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3559 - loss: 1.7404 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3486 - loss: 1.7466
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3474 - loss: 1.7507
[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3462 - loss: 1.7535
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[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3449 - loss: 1.7562
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3447 - loss: 1.7567 - val_accuracy: 0.3713 - val_loss: 1.6097
Epoch 75/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4531 - loss: 1.8335
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3663 - loss: 1.7893 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3549 - loss: 1.7821
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3492 - loss: 1.7803
[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3461 - loss: 1.7783
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3446 - loss: 1.7773
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3439 - loss: 1.7764
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3436 - loss: 1.7751
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3433 - loss: 1.7747 - val_accuracy: 0.3658 - val_loss: 1.5977
Epoch 76/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.3281 - loss: 1.7740
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3351 - loss: 1.8202 
[1m 70/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3381 - loss: 1.8026
[1m104/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3389 - loss: 1.7916
[1m143/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3396 - loss: 1.7847
[1m179/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3401 - loss: 1.7812
[1m219/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3404 - loss: 1.7782
[1m258/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3410 - loss: 1.7754
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3415 - loss: 1.7735 - val_accuracy: 0.3710 - val_loss: 1.5974
Epoch 77/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3594 - loss: 1.6832
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3356 - loss: 1.7926 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3391 - loss: 1.7828
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3409 - loss: 1.7771
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3416 - loss: 1.7740
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3422 - loss: 1.7706
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3425 - loss: 1.7688
[1m263/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3424 - loss: 1.7679
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3423 - loss: 1.7668 - val_accuracy: 0.3648 - val_loss: 1.5856
Epoch 78/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4844 - loss: 1.5315
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3823 - loss: 1.6695 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3685 - loss: 1.6970
[1m111/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3626 - loss: 1.7082
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3584 - loss: 1.7177
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3555 - loss: 1.7250
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3536 - loss: 1.7299
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3521 - loss: 1.7338
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3514 - loss: 1.7356 - val_accuracy: 0.3613 - val_loss: 1.5970
Epoch 79/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2656 - loss: 1.7487
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3113 - loss: 1.7336 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3217 - loss: 1.7441
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3282 - loss: 1.7471
[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3315 - loss: 1.7493
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3337 - loss: 1.7501
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3353 - loss: 1.7499
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3362 - loss: 1.7498
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3368 - loss: 1.7496 - val_accuracy: 0.3656 - val_loss: 1.5977
Epoch 80/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3750 - loss: 1.5960
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[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3628 - loss: 1.7023
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3592 - loss: 1.7117
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3566 - loss: 1.7172
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3544 - loss: 1.7208
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3531 - loss: 1.7231
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3521 - loss: 1.7258
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3515 - loss: 1.7277 - val_accuracy: 0.3765 - val_loss: 1.5963
Epoch 81/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3750 - loss: 1.8784
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3662 - loss: 1.6952 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3571 - loss: 1.7115
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3547 - loss: 1.7205
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3535 - loss: 1.7259
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3524 - loss: 1.7299
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3514 - loss: 1.7329
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3505 - loss: 1.7350
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3501 - loss: 1.7358 - val_accuracy: 0.3674 - val_loss: 1.6068
Epoch 82/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3281 - loss: 1.8157
[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3382 - loss: 1.7496 
[1m 82/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3416 - loss: 1.7502
[1m122/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3430 - loss: 1.7473
[1m162/292[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3443 - loss: 1.7445
[1m200/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3446 - loss: 1.7436
[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3447 - loss: 1.7432
[1m276/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3450 - loss: 1.7425
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3451 - loss: 1.7424 - val_accuracy: 0.3706 - val_loss: 1.6051

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:13[0m 860ms/step
[1m60/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 854us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 22: 36.76 [%]
F1-score capturado en la ejecución 22: 36.36 [%]

=== EJECUCIÓN 23 ===

--- TRAIN (ejecución 23) ---

--- TEST (ejecución 23) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18676, 6, 250)

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[1m 64/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 803us/step  
[1m139/584[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 735us/step
[1m210/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 727us/step
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[1m350/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 724us/step
[1m417/584[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 728us/step
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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
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[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 65/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 786us/step
[1m130/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 781us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 37.06 [%]
Global F1 score (validation) = 36.9 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[2.1104027e-01 1.7329851e-01 2.0389383e-01 ... 2.8027471e-06
  1.5430100e-01 6.8618655e-02]
 [2.1043949e-01 1.9148742e-01 2.1426152e-01 ... 1.0438617e-05
  1.4752518e-01 3.6656417e-02]
 [2.0060544e-01 1.7693597e-01 2.0866480e-01 ... 1.4501060e-05
  1.7496151e-01 4.6353299e-02]
 ...
 [1.9892995e-01 2.0310448e-01 2.1008015e-01 ... 1.7769145e-05
  1.6469978e-01 2.0741597e-02]
 [1.7505667e-01 2.1062140e-01 1.9254644e-01 ... 3.8354646e-04
  1.6174200e-01 1.3452541e-02]
 [1.5620393e-01 1.8763828e-01 1.6809741e-01 ... 4.3122476e-04
  1.8901578e-01 1.4836597e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 43.46 [%]
Global accuracy score (test) = 34.82 [%]
Global F1 score (train) = 42.02 [%]
Global F1 score (test) = 34.41 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.32      0.12      0.18       184
 CAMINAR CON MÓVIL O LIBRO       0.23      0.38      0.28       184
       CAMINAR USUAL SPEED       0.21      0.34      0.26       184
            CAMINAR ZIGZAG       0.17      0.08      0.10       184
          DE PIE BARRIENDO       0.19      0.36      0.25       184
   DE PIE DOBLANDO TOALLAS       0.25      0.15      0.19       184
    DE PIE MOVIENDO LIBROS       0.27      0.31      0.29       184
          DE PIE USANDO PC       0.40      0.62      0.49       184
        FASE REPOSO CON K5       0.72      0.74      0.73       184
INCREMENTAL CICLOERGOMETRO       0.90      0.61      0.72       184
           SENTADO LEYENDO       0.30      0.29      0.30       184
         SENTADO USANDO PC       0.15      0.08      0.10       184
      SENTADO VIENDO LA TV       0.27      0.26      0.27       184
   SUBIR Y BAJAR ESCALERAS       0.31      0.17      0.22       184
                    TROTAR       0.81      0.75      0.77       161

                  accuracy                           0.35      2737
                 macro avg       0.37      0.35      0.34      2737
              weighted avg       0.36      0.35      0.34      2737

2025-10-28 14:27:09.623393: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 14:27:09.634718: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1761658029.648313 2398263 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761658029.652424 2398263 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761658029.662479 2398263 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761658029.662506 2398263 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761658029.662508 2398263 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761658029.662510 2398263 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 14:27:09.665520: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1761658032.065706 2398263 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13733 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/110
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761658033.785390 2398385 service.cc:152] XLA service 0x7389500050d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761658033.785453 2398385 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 14:27:13.821935: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
I0000 00:00:1761658033.993016 2398385 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761658036.277549 2398385 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16:50[0m 3s/step - accuracy: 0.0781 - loss: 3.7362
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0706 - loss: 3.6020  
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0722 - loss: 3.5701
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[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0770 - loss: 3.5190
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[1m237/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0807 - loss: 3.4839
[1m279/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.0822 - loss: 3.4692
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - accuracy: 0.0826 - loss: 3.4650
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 16ms/step - accuracy: 0.0826 - loss: 3.4647 - val_accuracy: 0.1608 - val_loss: 2.4657
Epoch 2/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1084 - loss: 3.1947 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1027 - loss: 3.2059
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1013 - loss: 3.2038
[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1010 - loss: 3.2011
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1012 - loss: 3.1982
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1017 - loss: 3.1941
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Epoch 3/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1228 - loss: 3.0751 
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Epoch 4/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1153 - loss: 2.9606 
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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1205 - loss: 2.9472
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Epoch 5/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1363 - loss: 2.8581 
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[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1361 - loss: 2.8424
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Epoch 6/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1245 - loss: 2.7999 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1325 - loss: 2.7822
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[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1381 - loss: 2.7663
[1m199/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1388 - loss: 2.7636
[1m241/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1394 - loss: 2.7605
[1m280/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1398 - loss: 2.7580
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1399 - loss: 2.7572 - val_accuracy: 0.1974 - val_loss: 2.2799
Epoch 7/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1526 - loss: 2.6840 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1545 - loss: 2.6816
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[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1547 - loss: 2.6788
[1m201/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1549 - loss: 2.6761
[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1549 - loss: 2.6737
[1m277/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1548 - loss: 2.6719
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Epoch 8/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1705 - loss: 2.5794 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1667 - loss: 2.5944
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1612 - loss: 2.6007
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1610 - loss: 2.6000
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1610 - loss: 2.5992 - val_accuracy: 0.2124 - val_loss: 2.2054
Epoch 9/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2031 - loss: 2.5337
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1625 - loss: 2.5910 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1661 - loss: 2.5671
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1657 - loss: 2.5522
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1656 - loss: 2.5497
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1655 - loss: 2.5482 - val_accuracy: 0.2305 - val_loss: 2.1430
Epoch 10/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1719 - loss: 2.5850
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1714 - loss: 2.4832 
[1m 69/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1707 - loss: 2.4775
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[1m144/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1712 - loss: 2.4731
[1m179/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1718 - loss: 2.4722
[1m216/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1720 - loss: 2.4716
[1m257/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1725 - loss: 2.4704
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1727 - loss: 2.4695 - val_accuracy: 0.2296 - val_loss: 2.1013
Epoch 11/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1875 - loss: 2.5512
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1718 - loss: 2.4688 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1740 - loss: 2.4650
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[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1776 - loss: 2.4568
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1778 - loss: 2.4541
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1782 - loss: 2.4516
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1786 - loss: 2.4491
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1789 - loss: 2.4476 - val_accuracy: 0.2355 - val_loss: 2.0749
Epoch 12/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.1406 - loss: 2.5190
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1845 - loss: 2.4172 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1845 - loss: 2.4086
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1855 - loss: 2.4035
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1857 - loss: 2.4009
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1856 - loss: 2.3995
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1855 - loss: 2.3977
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1856 - loss: 2.3955
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1857 - loss: 2.3945 - val_accuracy: 0.2483 - val_loss: 2.0332
Epoch 13/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1875 - loss: 2.3709
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2018 - loss: 2.3596 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1998 - loss: 2.3515
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1975 - loss: 2.3492
[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1964 - loss: 2.3471
[1m198/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1957 - loss: 2.3460
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1953 - loss: 2.3456
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1951 - loss: 2.3450
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1950 - loss: 2.3445 - val_accuracy: 0.2516 - val_loss: 2.0186
Epoch 14/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1884 - loss: 2.2747 
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Epoch 15/110

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Epoch 16/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1993 - loss: 2.2193 
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[1m276/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2067 - loss: 2.2390
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2069 - loss: 2.2394 - val_accuracy: 0.2660 - val_loss: 1.9414
Epoch 17/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2045 - loss: 2.2549 
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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2092 - loss: 2.2380
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2100 - loss: 2.2339
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2105 - loss: 2.2311
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2109 - loss: 2.2293
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2111 - loss: 2.2280 - val_accuracy: 0.2912 - val_loss: 1.9195
Epoch 18/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2054 - loss: 2.2258 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2100 - loss: 2.2099
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[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2139 - loss: 2.2017
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2149 - loss: 2.1999
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2154 - loss: 2.1989
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2160 - loss: 2.1980
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Epoch 19/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2294 - loss: 2.1826 
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2239 - loss: 2.1768
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2236 - loss: 2.1758
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2235 - loss: 2.1752 - val_accuracy: 0.2705 - val_loss: 1.9059
Epoch 20/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2333 - loss: 2.1357 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2292 - loss: 2.1379
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2265 - loss: 2.1438
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2259 - loss: 2.1463
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Epoch 21/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2031 - loss: 2.4434
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2240 - loss: 2.1882 
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[1m202/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2307 - loss: 2.1562
[1m244/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2313 - loss: 2.1541
[1m284/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2319 - loss: 2.1518
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2321 - loss: 2.1513 - val_accuracy: 0.2849 - val_loss: 1.8805
Epoch 22/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3906 - loss: 2.0495
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2668 - loss: 2.1101 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2539 - loss: 2.1141
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[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2444 - loss: 2.1159
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2417 - loss: 2.1169
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2400 - loss: 2.1175
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2389 - loss: 2.1172
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2382 - loss: 2.1171 - val_accuracy: 0.2825 - val_loss: 1.8828
Epoch 23/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2500 - loss: 1.9957
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2533 - loss: 2.0725 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2535 - loss: 2.0769
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2507 - loss: 2.0833
[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2482 - loss: 2.0881
[1m198/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2467 - loss: 2.0905
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2454 - loss: 2.0926
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2444 - loss: 2.0943
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2441 - loss: 2.0949 - val_accuracy: 0.2981 - val_loss: 1.8533
Epoch 24/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2031 - loss: 2.3291
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2391 - loss: 2.0981 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2399 - loss: 2.0964
[1m119/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2408 - loss: 2.0978
[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2417 - loss: 2.0985
[1m198/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2424 - loss: 2.0972
[1m239/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2426 - loss: 2.0963
[1m281/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2425 - loss: 2.0958
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2425 - loss: 2.0957 - val_accuracy: 0.3006 - val_loss: 1.8461
Epoch 25/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2582 - loss: 2.0663 
[1m 65/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2537 - loss: 2.0712
[1m 98/292[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2513 - loss: 2.0747
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[1m218/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2481 - loss: 2.0763
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2473 - loss: 2.0764 - val_accuracy: 0.2953 - val_loss: 1.8373
Epoch 26/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2550 - loss: 2.0594 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2534 - loss: 2.0592
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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2514 - loss: 2.0658
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2513 - loss: 2.0667 - val_accuracy: 0.3025 - val_loss: 1.8349
Epoch 27/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2557 - loss: 2.0764 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2531 - loss: 2.0628
[1m120/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2522 - loss: 2.0638
[1m160/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2512 - loss: 2.0660
[1m201/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2507 - loss: 2.0669
[1m239/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2503 - loss: 2.0672
[1m278/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2501 - loss: 2.0669
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2500 - loss: 2.0667 - val_accuracy: 0.3016 - val_loss: 1.8364
Epoch 28/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2794 - loss: 2.0331 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2668 - loss: 2.0486
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2617 - loss: 2.0542
[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2590 - loss: 2.0542
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2574 - loss: 2.0539
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2565 - loss: 2.0534
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2558 - loss: 2.0525
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2555 - loss: 2.0521 - val_accuracy: 0.3012 - val_loss: 1.8293
Epoch 29/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2612 - loss: 2.0372 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2594 - loss: 2.0449
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2601 - loss: 2.0432
[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2610 - loss: 2.0396
[1m197/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2615 - loss: 2.0377
[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2615 - loss: 2.0370
[1m275/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2614 - loss: 2.0367
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2612 - loss: 2.0367 - val_accuracy: 0.3080 - val_loss: 1.8170
Epoch 30/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2514 - loss: 2.0638 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2546 - loss: 2.0563
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Epoch 31/110

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Epoch 32/110

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Epoch 33/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3594 - loss: 1.7891
[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2902 - loss: 1.9625 
[1m 82/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2887 - loss: 1.9734
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[1m240/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2816 - loss: 1.9850
[1m280/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2807 - loss: 1.9866
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2805 - loss: 1.9872 - val_accuracy: 0.3197 - val_loss: 1.7902
Epoch 34/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2812 - loss: 1.9628
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2766 - loss: 2.0371 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2778 - loss: 2.0207
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[1m143/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2773 - loss: 2.0127
[1m179/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2766 - loss: 2.0099
[1m216/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2760 - loss: 2.0082
[1m254/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2755 - loss: 2.0067
[1m291/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2750 - loss: 2.0054
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2750 - loss: 2.0053 - val_accuracy: 0.3173 - val_loss: 1.7924
Epoch 35/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2656 - loss: 1.9315
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2599 - loss: 1.9794 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2625 - loss: 1.9850
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[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2659 - loss: 1.9828
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2668 - loss: 1.9829
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2679 - loss: 1.9836
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2686 - loss: 1.9844
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Epoch 36/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2811 - loss: 2.0047 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2817 - loss: 1.9982
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Epoch 37/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2903 - loss: 1.9827 
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2782 - loss: 1.9853
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Epoch 38/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2750 - loss: 1.9599 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2794 - loss: 1.9577
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[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2817 - loss: 1.9535
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2819 - loss: 1.9550
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2823 - loss: 1.9558
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2824 - loss: 1.9562 - val_accuracy: 0.3265 - val_loss: 1.7612
Epoch 39/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3594 - loss: 1.9712
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3020 - loss: 1.9205 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3019 - loss: 1.9316
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[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2995 - loss: 1.9401
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2989 - loss: 1.9418
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2985 - loss: 1.9427
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2980 - loss: 1.9439
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2977 - loss: 1.9449 - val_accuracy: 0.3371 - val_loss: 1.7511
Epoch 40/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.2031 - loss: 1.9760
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2918 - loss: 1.9343 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2925 - loss: 1.9374
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[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2926 - loss: 1.9391
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2928 - loss: 1.9395
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2930 - loss: 1.9398
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2930 - loss: 1.9406
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Epoch 41/110

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[1m 34/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2850 - loss: 1.9723 
[1m 69/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2867 - loss: 1.9642
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Epoch 42/110

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Epoch 43/110

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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2974 - loss: 1.9210 - val_accuracy: 0.3315 - val_loss: 1.7257
Epoch 44/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3047 - loss: 1.9275 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3031 - loss: 1.9291
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[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3014 - loss: 1.9221
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3014 - loss: 1.9202
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3015 - loss: 1.9191
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3014 - loss: 1.9185 - val_accuracy: 0.3330 - val_loss: 1.7230
Epoch 45/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 26ms/step - accuracy: 0.3594 - loss: 1.8161
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2962 - loss: 1.9228 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2986 - loss: 1.9164
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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2992 - loss: 1.9167
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2989 - loss: 1.9172
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2990 - loss: 1.9174
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2992 - loss: 1.9172
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2993 - loss: 1.9171 - val_accuracy: 0.3391 - val_loss: 1.7212
Epoch 46/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3138 - loss: 1.9340 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3138 - loss: 1.9113
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3096 - loss: 1.9069
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3088 - loss: 1.9075
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Epoch 47/110

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Epoch 48/110

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Epoch 49/110

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[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3153 - loss: 1.8784
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3152 - loss: 1.8775 - val_accuracy: 0.3502 - val_loss: 1.6970
Epoch 50/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3418 - loss: 1.8562 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3306 - loss: 1.8761
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[1m147/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3202 - loss: 1.8892
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3175 - loss: 1.8911
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3160 - loss: 1.8917
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3148 - loss: 1.8920
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Epoch 51/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2988 - loss: 1.9062 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3046 - loss: 1.8888
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[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3086 - loss: 1.8769
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3091 - loss: 1.8765
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3097 - loss: 1.8765
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Epoch 52/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3390 - loss: 1.8468 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3299 - loss: 1.8481
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Epoch 53/110

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Epoch 54/110

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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3163 - loss: 1.8460 - val_accuracy: 0.3491 - val_loss: 1.6794
Epoch 55/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3281 - loss: 1.8308
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3123 - loss: 1.8696 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3190 - loss: 1.8578
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3217 - loss: 1.8478
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3218 - loss: 1.8471
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3219 - loss: 1.8469 - val_accuracy: 0.3634 - val_loss: 1.6714
Epoch 56/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3326 - loss: 1.8536 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3288 - loss: 1.8444
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[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3245 - loss: 1.8423
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3239 - loss: 1.8419
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3234 - loss: 1.8417
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3230 - loss: 1.8417
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3227 - loss: 1.8416 - val_accuracy: 0.3624 - val_loss: 1.6744
Epoch 57/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3906 - loss: 1.7005
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3398 - loss: 1.8291 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3287 - loss: 1.8469
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[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3199 - loss: 1.8480
[1m275/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3197 - loss: 1.8481
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Epoch 58/110

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Epoch 59/110

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Epoch 60/110

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Epoch 61/110

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[1m216/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3320 - loss: 1.7924
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Epoch 62/110

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Epoch 63/110

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Epoch 64/110

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Epoch 65/110

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Epoch 66/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3906 - loss: 1.6195
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3308 - loss: 1.7946 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3302 - loss: 1.7987
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[1m237/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3320 - loss: 1.8015
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3325 - loss: 1.8008 - val_accuracy: 0.3700 - val_loss: 1.6377
Epoch 67/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2812 - loss: 1.7648
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3198 - loss: 1.8149 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3238 - loss: 1.8123
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[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3256 - loss: 1.8096
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3276 - loss: 1.8080
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3283 - loss: 1.8073
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Epoch 68/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3598 - loss: 1.7462 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3473 - loss: 1.7722
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3377 - loss: 1.7965
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3371 - loss: 1.7980
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Epoch 69/110

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Epoch 70/110

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Epoch 71/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3444 - loss: 1.7620 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3383 - loss: 1.7695
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3342 - loss: 1.7866
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3341 - loss: 1.7881
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3342 - loss: 1.7884 - val_accuracy: 0.3730 - val_loss: 1.6333
Epoch 72/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3274 - loss: 1.7738 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3341 - loss: 1.7740
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[1m162/292[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3356 - loss: 1.7746
[1m203/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3357 - loss: 1.7751
[1m240/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3356 - loss: 1.7750
[1m281/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3358 - loss: 1.7751
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3359 - loss: 1.7753 - val_accuracy: 0.3658 - val_loss: 1.6438
Epoch 73/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3174 - loss: 1.8185 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3281 - loss: 1.7938
[1m121/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3336 - loss: 1.7844
[1m161/292[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3361 - loss: 1.7815
[1m203/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3371 - loss: 1.7809
[1m245/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3375 - loss: 1.7819
[1m285/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3378 - loss: 1.7821
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Epoch 74/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3282 - loss: 1.7956 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3328 - loss: 1.7880
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[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3379 - loss: 1.7764
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3381 - loss: 1.7753
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3383 - loss: 1.7749
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3384 - loss: 1.7748 - val_accuracy: 0.3739 - val_loss: 1.6298
Epoch 75/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3594 - loss: 1.7358
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3268 - loss: 1.7754 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3306 - loss: 1.7777
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3327 - loss: 1.7755
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3340 - loss: 1.7743
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3350 - loss: 1.7741
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3359 - loss: 1.7737
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3364 - loss: 1.7739
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3364 - loss: 1.7741 - val_accuracy: 0.3660 - val_loss: 1.6335
Epoch 76/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3438 - loss: 1.9285
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3480 - loss: 1.7955 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3470 - loss: 1.7735
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3467 - loss: 1.7681
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3463 - loss: 1.7668
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3464 - loss: 1.7658
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3462 - loss: 1.7653
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3458 - loss: 1.7651
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3457 - loss: 1.7653 - val_accuracy: 0.3737 - val_loss: 1.6294
Epoch 77/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.6532
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3740 - loss: 1.7098 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3628 - loss: 1.7328
[1m111/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3593 - loss: 1.7422
[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3563 - loss: 1.7482
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3531 - loss: 1.7536
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3513 - loss: 1.7565
[1m262/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3500 - loss: 1.7589
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3492 - loss: 1.7600 - val_accuracy: 0.3765 - val_loss: 1.6239
Epoch 78/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2656 - loss: 1.6279
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3472 - loss: 1.7164 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3478 - loss: 1.7345
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3482 - loss: 1.7417
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3484 - loss: 1.7464
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3486 - loss: 1.7499
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3488 - loss: 1.7516
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3489 - loss: 1.7527
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3489 - loss: 1.7533 - val_accuracy: 0.3824 - val_loss: 1.6294
Epoch 79/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 24ms/step - accuracy: 0.4219 - loss: 1.6540
[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3461 - loss: 1.7390 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3459 - loss: 1.7480
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3468 - loss: 1.7481
[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3467 - loss: 1.7484
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3463 - loss: 1.7481
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3461 - loss: 1.7484
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3457 - loss: 1.7491
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Epoch 80/110

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Epoch 81/110

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[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3503 - loss: 1.7237
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Epoch 82/110

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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3462 - loss: 1.7452 - val_accuracy: 0.3724 - val_loss: 1.6153
Epoch 83/110

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[1m 42/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3569 - loss: 1.7276 
[1m 83/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3551 - loss: 1.7394
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[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3532 - loss: 1.7426
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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3520 - loss: 1.7460
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3516 - loss: 1.7469
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Epoch 84/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3435 - loss: 1.7800 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3390 - loss: 1.7803
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[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3390 - loss: 1.7726
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3397 - loss: 1.7663
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Epoch 85/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3293 - loss: 1.7316 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3370 - loss: 1.7330
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3405 - loss: 1.7321
[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3426 - loss: 1.7333
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3440 - loss: 1.7345
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3446 - loss: 1.7357
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3448 - loss: 1.7369
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3448 - loss: 1.7376 - val_accuracy: 0.3724 - val_loss: 1.6188
Epoch 86/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2969 - loss: 1.6759
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3395 - loss: 1.7268 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3425 - loss: 1.7411
[1m120/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3431 - loss: 1.7503
[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3434 - loss: 1.7529
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3448 - loss: 1.7522
[1m237/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3459 - loss: 1.7510
[1m276/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3468 - loss: 1.7501
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3471 - loss: 1.7497 - val_accuracy: 0.3841 - val_loss: 1.6177
Epoch 87/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3438 - loss: 1.7478
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3600 - loss: 1.7029 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3563 - loss: 1.7172
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3530 - loss: 1.7263
[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3503 - loss: 1.7299
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3493 - loss: 1.7326
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3491 - loss: 1.7335
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3493 - loss: 1.7337
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3495 - loss: 1.7340 - val_accuracy: 0.3721 - val_loss: 1.6230

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:13[0m 861ms/step
[1m67/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 761us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 9ms/step
Saved model to disk.

Accuracy capturado en la ejecución 23: 34.82 [%]
F1-score capturado en la ejecución 23: 34.41 [%]

=== EJECUCIÓN 24 ===

--- TRAIN (ejecución 24) ---

--- TEST (ejecución 24) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18676, 6, 250)

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:50[0m 909ms/step
[1m 65/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 785us/step  
[1m132/584[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 770us/step
[1m199/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 764us/step
[1m256/584[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 790us/step
[1m319/584[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 791us/step
[1m389/584[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 779us/step
[1m462/584[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 764us/step
[1m528/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 764us/step
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m70/86[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 729us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 8ms/step

[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 61/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 844us/step
[1m136/169[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 752us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 37.21 [%]
Global F1 score (validation) = 36.73 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[2.06535056e-01 1.86231896e-01 2.09844038e-01 ... 1.15668254e-05
  1.43904194e-01 4.41101789e-02]
 [1.97291046e-01 2.12698027e-01 2.16242433e-01 ... 1.35250084e-05
  1.48094431e-01 1.53730530e-02]
 [1.94946855e-01 2.00842276e-01 2.01046541e-01 ... 1.26131308e-05
  1.69078574e-01 2.50560101e-02]
 ...
 [2.03886002e-01 2.15347141e-01 2.19908208e-01 ... 1.49866555e-05
  1.46196723e-01 1.16079580e-02]
 [2.09190503e-01 2.01120824e-01 2.08370671e-01 ... 8.17021737e-06
  1.58988029e-01 1.64762698e-02]
 [3.16343978e-02 4.28045765e-02 3.37573811e-02 ... 2.61391001e-03
  5.51184826e-02 2.65249168e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 43.18 [%]
Global accuracy score (test) = 36.54 [%]
Global F1 score (train) = 41.93 [%]
Global F1 score (test) = 35.83 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.32      0.22      0.26       184
 CAMINAR CON MÓVIL O LIBRO       0.25      0.48      0.33       184
       CAMINAR USUAL SPEED       0.26      0.25      0.25       184
            CAMINAR ZIGZAG       0.14      0.14      0.14       184
          DE PIE BARRIENDO       0.27      0.47      0.34       184
   DE PIE DOBLANDO TOALLAS       0.47      0.08      0.14       184
    DE PIE MOVIENDO LIBROS       0.27      0.38      0.32       184
          DE PIE USANDO PC       0.38      0.60      0.47       184
        FASE REPOSO CON K5       0.62      0.79      0.70       184
INCREMENTAL CICLOERGOMETRO       0.91      0.59      0.71       184
           SENTADO LEYENDO       0.33      0.27      0.29       184
         SENTADO USANDO PC       0.09      0.05      0.07       184
      SENTADO VIENDO LA TV       0.30      0.28      0.29       184
   SUBIR Y BAJAR ESCALERAS       0.38      0.19      0.25       184
                    TROTAR       0.90      0.75      0.81       161

                  accuracy                           0.37      2737
                 macro avg       0.39      0.37      0.36      2737
              weighted avg       0.39      0.37      0.35      2737

2025-10-28 14:28:20.753056: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 14:28:20.764360: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1761658100.777617 2407376 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761658100.781867 2407376 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761658100.791770 2407376 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761658100.791791 2407376 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761658100.791794 2407376 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761658100.791795 2407376 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 14:28:20.795053: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1761658103.178860 2407376 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13733 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/110
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761658104.884845 2407508 service.cc:152] XLA service 0x783568004150 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761658104.884919 2407508 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 14:28:24.921804: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
I0000 00:00:1761658105.086733 2407508 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761658107.367501 2407508 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/110

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Epoch 3/110

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Epoch 4/110

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Epoch 5/110

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Epoch 6/110

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Epoch 7/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1560 - loss: 2.6780 
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1552 - loss: 2.6546
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1554 - loss: 2.6517
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1554 - loss: 2.6506 - val_accuracy: 0.2235 - val_loss: 2.2336
Epoch 8/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1507 - loss: 2.5909 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1537 - loss: 2.5905
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[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1541 - loss: 2.5924
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1547 - loss: 2.5909
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1549 - loss: 2.5899
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1551 - loss: 2.5890 - val_accuracy: 0.2364 - val_loss: 2.1923
Epoch 9/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1642 - loss: 2.5368 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1652 - loss: 2.5288
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1655 - loss: 2.5282
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1658 - loss: 2.5272
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Epoch 10/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1729 - loss: 2.4982 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1723 - loss: 2.4872
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1734 - loss: 2.4699 - val_accuracy: 0.2551 - val_loss: 2.1209
Epoch 11/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2021 - loss: 2.3657 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1949 - loss: 2.3857
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1917 - loss: 2.3940
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[1m219/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1882 - loss: 2.3995
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1874 - loss: 2.3997 - val_accuracy: 0.2463 - val_loss: 2.0933
Epoch 12/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1933 - loss: 2.3690 
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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1912 - loss: 2.3805
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1909 - loss: 2.3794
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1908 - loss: 2.3786 - val_accuracy: 0.2640 - val_loss: 2.0736
Epoch 13/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1815 - loss: 2.3567 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1855 - loss: 2.3530
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1866 - loss: 2.3492
[1m144/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1877 - loss: 2.3466
[1m181/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1882 - loss: 2.3451
[1m219/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1884 - loss: 2.3444
[1m256/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1883 - loss: 2.3435
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1885 - loss: 2.3422 - val_accuracy: 0.2677 - val_loss: 2.0318
Epoch 14/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2014 - loss: 2.2754 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2035 - loss: 2.2806
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2053 - loss: 2.2840
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2055 - loss: 2.2862
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2060 - loss: 2.2867
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2067 - loss: 2.2871
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2071 - loss: 2.2872
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2072 - loss: 2.2874 - val_accuracy: 0.2716 - val_loss: 2.0016
Epoch 15/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2007 - loss: 2.2637 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2035 - loss: 2.2689
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Epoch 16/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 25ms/step - accuracy: 0.2031 - loss: 2.3097
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2215 - loss: 2.2219 
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[1m216/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2185 - loss: 2.2290
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2172 - loss: 2.2302 - val_accuracy: 0.2770 - val_loss: 1.9574
Epoch 17/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1875 - loss: 2.3224
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2128 - loss: 2.2339 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2129 - loss: 2.2286
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[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2142 - loss: 2.2213
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2152 - loss: 2.2178 - val_accuracy: 0.2814 - val_loss: 1.9286
Epoch 18/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.2500 - loss: 2.2585
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2226 - loss: 2.2036 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2240 - loss: 2.2023
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[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2231 - loss: 2.2009
[1m200/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2222 - loss: 2.2002
[1m243/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2218 - loss: 2.1985
[1m282/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2220 - loss: 2.1968
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2221 - loss: 2.1963 - val_accuracy: 0.2799 - val_loss: 1.9414
Epoch 19/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2969 - loss: 2.0332
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2225 - loss: 2.1971 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2227 - loss: 2.1924
[1m120/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2243 - loss: 2.1878
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2253 - loss: 2.1840
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2255 - loss: 2.1808
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2252 - loss: 2.1781
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2252 - loss: 2.1759
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2252 - loss: 2.1746 - val_accuracy: 0.2825 - val_loss: 1.9068
Epoch 20/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1719 - loss: 2.2926
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2091 - loss: 2.1630 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2106 - loss: 2.1652
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2134 - loss: 2.1619
[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2151 - loss: 2.1593
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2166 - loss: 2.1574
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2179 - loss: 2.1559
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2189 - loss: 2.1545
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2198 - loss: 2.1532 - val_accuracy: 0.2766 - val_loss: 1.9049
Epoch 21/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2140 - loss: 2.1635 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2169 - loss: 2.1525
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Epoch 22/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2347 - loss: 2.0911 
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2402 - loss: 2.1029 - val_accuracy: 0.2883 - val_loss: 1.8692
Epoch 23/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2366 - loss: 2.0979 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2333 - loss: 2.0982
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[1m182/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2314 - loss: 2.0960
[1m216/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2319 - loss: 2.0959
[1m254/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2324 - loss: 2.0959
[1m290/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2329 - loss: 2.0960
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2330 - loss: 2.0960 - val_accuracy: 0.3084 - val_loss: 1.8479
Epoch 24/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2564 - loss: 2.0493 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2536 - loss: 2.0550
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2522 - loss: 2.0588
[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2511 - loss: 2.0625
[1m197/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2504 - loss: 2.0649
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2499 - loss: 2.0672
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2498 - loss: 2.0688
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2497 - loss: 2.0694 - val_accuracy: 0.2973 - val_loss: 1.8530
Epoch 25/110

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[1m 34/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2859 - loss: 1.9964 
[1m 70/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2763 - loss: 2.0323
[1m108/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2703 - loss: 2.0456
[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2669 - loss: 2.0522
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2652 - loss: 2.0543
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2640 - loss: 2.0558
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2630 - loss: 2.0568
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2622 - loss: 2.0572 - val_accuracy: 0.3238 - val_loss: 1.8395
Epoch 26/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2379 - loss: 2.0450 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2451 - loss: 2.0513
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2486 - loss: 2.0533
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2495 - loss: 2.0537
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2505 - loss: 2.0535
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2513 - loss: 2.0529
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2518 - loss: 2.0522 - val_accuracy: 0.3175 - val_loss: 1.8207
Epoch 27/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1406 - loss: 2.1693
[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2444 - loss: 2.0119 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2517 - loss: 2.0189
[1m119/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2550 - loss: 2.0222
[1m160/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2571 - loss: 2.0244
[1m200/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2583 - loss: 2.0263
[1m239/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2588 - loss: 2.0284
[1m278/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2589 - loss: 2.0308
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2590 - loss: 2.0315 - val_accuracy: 0.3001 - val_loss: 1.8190
Epoch 28/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2969 - loss: 2.0162
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2677 - loss: 2.0632 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2643 - loss: 2.0588
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2628 - loss: 2.0543
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2620 - loss: 2.0514
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2614 - loss: 2.0494
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2612 - loss: 2.0468
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2612 - loss: 2.0445
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2611 - loss: 2.0433 - val_accuracy: 0.3060 - val_loss: 1.8165
Epoch 29/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.3125 - loss: 2.0637
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2602 - loss: 2.0290 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2614 - loss: 2.0190
[1m111/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2600 - loss: 2.0190
[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2603 - loss: 2.0185
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2607 - loss: 2.0186
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2611 - loss: 2.0186
[1m263/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2612 - loss: 2.0189
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2612 - loss: 2.0190 - val_accuracy: 0.3166 - val_loss: 1.7996
Epoch 30/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2656 - loss: 1.8451
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2688 - loss: 1.9735 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2687 - loss: 1.9860
[1m119/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2678 - loss: 1.9913
[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2670 - loss: 1.9942
[1m198/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2671 - loss: 1.9956
[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2670 - loss: 1.9971
[1m277/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2669 - loss: 1.9986
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2668 - loss: 1.9992 - val_accuracy: 0.3105 - val_loss: 1.7976
Epoch 31/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3281 - loss: 1.9390
[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2712 - loss: 1.9994 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2719 - loss: 2.0022
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2715 - loss: 2.0033
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2710 - loss: 2.0037
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2708 - loss: 2.0053
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2707 - loss: 2.0060
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2706 - loss: 2.0066
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Epoch 32/110

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[1m 33/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2708 - loss: 1.9939 
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2790 - loss: 1.9795
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Epoch 33/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2805 - loss: 1.9744 
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Epoch 34/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3281 - loss: 1.8097
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2731 - loss: 1.9633 
[1m 82/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2771 - loss: 1.9695
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[1m237/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2783 - loss: 1.9703
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2791 - loss: 1.9697 - val_accuracy: 0.3323 - val_loss: 1.7698
Epoch 35/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2639 - loss: 2.0151 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2693 - loss: 2.0047
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[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2769 - loss: 1.9851
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2781 - loss: 1.9817
[1m263/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2792 - loss: 1.9784
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2800 - loss: 1.9764 - val_accuracy: 0.3297 - val_loss: 1.7716
Epoch 36/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3750 - loss: 1.7046
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2876 - loss: 1.9324 
[1m 69/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2847 - loss: 1.9412
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[1m177/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2809 - loss: 1.9555
[1m214/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2809 - loss: 1.9568
[1m250/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2812 - loss: 1.9569
[1m287/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2814 - loss: 1.9567
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Epoch 37/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2996 - loss: 1.9755 
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Epoch 38/110

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Epoch 39/110

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Epoch 40/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2822 - loss: 1.9624 
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2979 - loss: 1.9305
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2981 - loss: 1.9293 - val_accuracy: 0.3436 - val_loss: 1.7207
Epoch 41/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3280 - loss: 1.8820 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3175 - loss: 1.8903
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3090 - loss: 1.9022
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3078 - loss: 1.9039
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Epoch 42/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2830 - loss: 1.9096 
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Epoch 43/110

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Epoch 44/110

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Epoch 45/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3231 - loss: 1.8878 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3157 - loss: 1.8872
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[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3115 - loss: 1.8846
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3108 - loss: 1.8840
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3101 - loss: 1.8843
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3099 - loss: 1.8844 - val_accuracy: 0.3550 - val_loss: 1.6925
Epoch 46/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3155 - loss: 1.8561 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3145 - loss: 1.8630
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3134 - loss: 1.8669
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3120 - loss: 1.8710
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3112 - loss: 1.8725
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3108 - loss: 1.8741
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3109 - loss: 1.8750
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3110 - loss: 1.8751 - val_accuracy: 0.3619 - val_loss: 1.6847
Epoch 47/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2656 - loss: 1.8904
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3121 - loss: 1.8763 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3154 - loss: 1.8743
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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3150 - loss: 1.8746
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Epoch 48/110

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Epoch 49/110

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Epoch 50/110

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Epoch 51/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3144 - loss: 1.8917 
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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3220 - loss: 1.8660
[1m275/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3221 - loss: 1.8652
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3223 - loss: 1.8646 - val_accuracy: 0.3475 - val_loss: 1.6663
Epoch 52/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3049 - loss: 1.8559 
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[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3163 - loss: 1.8469
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3174 - loss: 1.8451
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Epoch 53/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3318 - loss: 1.8306 
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Epoch 54/110

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Epoch 55/110

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Epoch 56/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3383 - loss: 1.7775 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3358 - loss: 1.7816
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[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3311 - loss: 1.8002
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3306 - loss: 1.8034
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3303 - loss: 1.8052 - val_accuracy: 0.3547 - val_loss: 1.6556
Epoch 57/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3278 - loss: 1.7778 
[1m 82/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3270 - loss: 1.7916
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[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3279 - loss: 1.7982
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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3280 - loss: 1.8018
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3280 - loss: 1.8037
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3279 - loss: 1.8044 - val_accuracy: 0.3574 - val_loss: 1.6500
Epoch 58/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3136 - loss: 1.8185 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3189 - loss: 1.8224
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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3250 - loss: 1.8217
[1m275/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3257 - loss: 1.8208
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Epoch 59/110

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Epoch 60/110

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Epoch 61/110

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Epoch 62/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3532 - loss: 1.7928 
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3436 - loss: 1.7907
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3425 - loss: 1.7912
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3420 - loss: 1.7916 - val_accuracy: 0.3721 - val_loss: 1.6356
Epoch 63/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3413 - loss: 1.7358 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3387 - loss: 1.7581
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[1m160/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3358 - loss: 1.7782
[1m198/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3355 - loss: 1.7813
[1m239/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3353 - loss: 1.7831
[1m279/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3350 - loss: 1.7844
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Epoch 64/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3393 - loss: 1.7835 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3402 - loss: 1.7759
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Epoch 65/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3509 - loss: 1.7798 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3478 - loss: 1.7834
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Epoch 66/110

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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3436 - loss: 1.7810
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Epoch 67/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3125 - loss: 1.6572
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3273 - loss: 1.7419 
[1m 82/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3287 - loss: 1.7614
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[1m160/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3305 - loss: 1.7721
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3313 - loss: 1.7735
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3321 - loss: 1.7743
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3332 - loss: 1.7748
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3337 - loss: 1.7751 - val_accuracy: 0.3610 - val_loss: 1.6262
Epoch 68/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4062 - loss: 1.9230
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3415 - loss: 1.7934 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3413 - loss: 1.7842
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[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3382 - loss: 1.7865
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3378 - loss: 1.7858
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3375 - loss: 1.7852
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3376 - loss: 1.7847
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3377 - loss: 1.7843 - val_accuracy: 0.3615 - val_loss: 1.6232
Epoch 69/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 25ms/step - accuracy: 0.4375 - loss: 1.7563
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3430 - loss: 1.7511 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3431 - loss: 1.7503
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[1m213/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3385 - loss: 1.7612
[1m249/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3382 - loss: 1.7631
[1m289/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3378 - loss: 1.7649
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Epoch 70/110

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Epoch 71/110

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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3518 - loss: 1.7664
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Epoch 72/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3523 - loss: 1.7407 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3485 - loss: 1.7521
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[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3433 - loss: 1.7602
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3428 - loss: 1.7613 - val_accuracy: 0.3648 - val_loss: 1.6338
Epoch 73/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3510 - loss: 1.7590 
[1m 82/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3486 - loss: 1.7518
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[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3467 - loss: 1.7537
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3463 - loss: 1.7550
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3460 - loss: 1.7558
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3455 - loss: 1.7567
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3454 - loss: 1.7570 - val_accuracy: 0.3687 - val_loss: 1.6145
Epoch 74/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3350 - loss: 1.7589 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3401 - loss: 1.7677
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3428 - loss: 1.7696
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[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3440 - loss: 1.7684
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Epoch 75/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3321 - loss: 1.7786 
[1m 82/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3389 - loss: 1.7653
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[1m246/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3439 - loss: 1.7509
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3444 - loss: 1.7504 - val_accuracy: 0.3717 - val_loss: 1.6175
Epoch 76/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.4531 - loss: 1.6124
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3628 - loss: 1.7353 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3554 - loss: 1.7385
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3522 - loss: 1.7405
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3499 - loss: 1.7428
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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3482 - loss: 1.7464
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3479 - loss: 1.7484 - val_accuracy: 0.3610 - val_loss: 1.6183
Epoch 77/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.4219 - loss: 1.5487
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3539 - loss: 1.7413 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3515 - loss: 1.7526
[1m119/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3488 - loss: 1.7576
[1m160/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3481 - loss: 1.7574
[1m200/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3473 - loss: 1.7578
[1m239/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3469 - loss: 1.7576
[1m278/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3467 - loss: 1.7571
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3468 - loss: 1.7565 - val_accuracy: 0.3782 - val_loss: 1.6137
Epoch 78/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.3281 - loss: 1.7206
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3472 - loss: 1.7362 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3474 - loss: 1.7515
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3462 - loss: 1.7581
[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3454 - loss: 1.7610
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3451 - loss: 1.7618
[1m237/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3452 - loss: 1.7616
[1m278/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3455 - loss: 1.7606
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3457 - loss: 1.7601 - val_accuracy: 0.3747 - val_loss: 1.6101
Epoch 79/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.3281 - loss: 1.8259
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3306 - loss: 1.7904 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3351 - loss: 1.7809
[1m119/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3380 - loss: 1.7756
[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3399 - loss: 1.7703
[1m198/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3412 - loss: 1.7661
[1m237/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3422 - loss: 1.7627
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3431 - loss: 1.7602
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3435 - loss: 1.7591 - val_accuracy: 0.3778 - val_loss: 1.6149
Epoch 80/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.3750 - loss: 1.7714
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3522 - loss: 1.7546 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3522 - loss: 1.7561
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3532 - loss: 1.7534
[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3532 - loss: 1.7514
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3529 - loss: 1.7493
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3529 - loss: 1.7470
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3529 - loss: 1.7450
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Epoch 81/110

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Epoch 82/110

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Epoch 83/110

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Epoch 84/110

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[1m 33/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3617 - loss: 1.6740 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3591 - loss: 1.6910
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[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3591 - loss: 1.7077
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3587 - loss: 1.7101
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Epoch 85/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3537 - loss: 1.7056 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3526 - loss: 1.7204
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Epoch 86/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3261 - loss: 1.7232 
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Epoch 87/110

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Saved model to disk.

Accuracy capturado en la ejecución 24: 36.54 [%]
F1-score capturado en la ejecución 24: 35.83 [%]

=== EJECUCIÓN 25 ===

--- TRAIN (ejecución 25) ---

--- TEST (ejecución 25) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18676, 6, 250)

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[1m 63/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 807us/step
[1m133/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 762us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 37.41 [%]
Global F1 score (validation) = 36.69 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[2.0187849e-01 1.9850211e-01 2.0281169e-01 ... 6.9074003e-06
  1.6649966e-01 1.5516187e-02]
 [2.0055328e-01 2.0624359e-01 2.0732081e-01 ... 7.5748299e-06
  1.5665813e-01 6.5255468e-03]
 [1.8327786e-01 1.8160781e-01 1.8642007e-01 ... 1.5382792e-04
  1.9286415e-01 5.2111998e-02]
 ...
 [1.9736725e-01 2.0888154e-01 2.0176174e-01 ... 4.8703307e-05
  1.6029745e-01 1.1844621e-02]
 [1.9436088e-01 1.8985134e-01 2.0031533e-01 ... 2.4946112e-05
  1.6650827e-01 3.1722330e-02]
 [5.1251564e-02 7.0014223e-02 5.6883719e-02 ... 6.4427028e-03
  7.1753949e-02 3.3269434e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 44.14 [%]
Global accuracy score (test) = 35.51 [%]
Global F1 score (train) = 42.34 [%]
Global F1 score (test) = 33.73 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.26      0.33      0.29       184
 CAMINAR CON MÓVIL O LIBRO       0.22      0.34      0.27       184
       CAMINAR USUAL SPEED       0.00      0.00      0.00       184
            CAMINAR ZIGZAG       0.19      0.29      0.23       184
          DE PIE BARRIENDO       0.28      0.51      0.37       184
   DE PIE DOBLANDO TOALLAS       0.29      0.12      0.18       184
    DE PIE MOVIENDO LIBROS       0.30      0.32      0.31       184
          DE PIE USANDO PC       0.42      0.62      0.50       184
        FASE REPOSO CON K5       0.56      0.76      0.64       184
INCREMENTAL CICLOERGOMETRO       0.91      0.60      0.72       184
           SENTADO LEYENDO       0.26      0.28      0.27       184
         SENTADO USANDO PC       0.12      0.04      0.06       184
      SENTADO VIENDO LA TV       0.32      0.32      0.32       184
   SUBIR Y BAJAR ESCALERAS       0.25      0.10      0.15       184
                    TROTAR       0.79      0.73      0.76       161

                  accuracy                           0.36      2737
                 macro avg       0.34      0.36      0.34      2737
              weighted avg       0.34      0.36      0.33      2737

2025-10-28 14:29:32.196682: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 14:29:32.208118: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1761658172.221623 2416549 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761658172.226002 2416549 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761658172.236035 2416549 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761658172.236058 2416549 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761658172.236060 2416549 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761658172.236062 2416549 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 14:29:32.239495: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1761658174.627803 2416549 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13733 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/110
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761658176.364632 2416653 service.cc:152] XLA service 0x7ca20000cb50 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761658176.364701 2416653 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 14:29:36.404822: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
I0000 00:00:1761658176.569329 2416653 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761658178.854178 2416653 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/110

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Epoch 3/110

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Epoch 4/110

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Epoch 5/110

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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1242 - loss: 2.8697
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1257 - loss: 2.8625 - val_accuracy: 0.2000 - val_loss: 2.3444
Epoch 6/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1406 - loss: 2.8480
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1299 - loss: 2.7900 
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[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1390 - loss: 2.7668
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1406 - loss: 2.7566 - val_accuracy: 0.2113 - val_loss: 2.3051
Epoch 7/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1544 - loss: 2.6877 
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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1451 - loss: 2.7031
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1449 - loss: 2.7019
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1448 - loss: 2.7003
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1452 - loss: 2.6965 - val_accuracy: 0.2181 - val_loss: 2.2753
Epoch 8/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1562 - loss: 2.5911
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1606 - loss: 2.6508 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1600 - loss: 2.6352
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[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1578 - loss: 2.6288
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1570 - loss: 2.6284
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1566 - loss: 2.6277
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1564 - loss: 2.6266
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1563 - loss: 2.6257 - val_accuracy: 0.2283 - val_loss: 2.2377
Epoch 9/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1406 - loss: 2.6845
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1461 - loss: 2.5988 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1514 - loss: 2.5953
[1m119/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1543 - loss: 2.5931
[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1561 - loss: 2.5906
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1572 - loss: 2.5880
[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1582 - loss: 2.5851
[1m275/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1590 - loss: 2.5828
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1593 - loss: 2.5818 - val_accuracy: 0.2233 - val_loss: 2.1971
Epoch 10/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1875 - loss: 2.5140
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1630 - loss: 2.5332 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1618 - loss: 2.5308
[1m119/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1630 - loss: 2.5279
[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1642 - loss: 2.5249
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1651 - loss: 2.5217
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1658 - loss: 2.5189
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1664 - loss: 2.5162
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Epoch 11/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1875 - loss: 2.4016
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1938 - loss: 2.4141 
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Epoch 12/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1562 - loss: 2.3834
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1857 - loss: 2.4394 
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[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1891 - loss: 2.4296
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[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1899 - loss: 2.4215
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1900 - loss: 2.4184
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1901 - loss: 2.4163 - val_accuracy: 0.2427 - val_loss: 2.0936
Epoch 13/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1875 - loss: 2.5137
[1m 31/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1866 - loss: 2.4096 
[1m 67/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1881 - loss: 2.3911
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[1m143/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1892 - loss: 2.3754
[1m181/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1894 - loss: 2.3724
[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1896 - loss: 2.3699
[1m262/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1898 - loss: 2.3673
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1899 - loss: 2.3656 - val_accuracy: 0.2533 - val_loss: 2.0619
Epoch 14/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.1250 - loss: 2.5617
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1938 - loss: 2.3578 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1958 - loss: 2.3516
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1958 - loss: 2.3467
[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1958 - loss: 2.3428
[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1963 - loss: 2.3395
[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1966 - loss: 2.3366
[1m258/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1971 - loss: 2.3336
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1975 - loss: 2.3314 - val_accuracy: 0.2407 - val_loss: 2.0280
Epoch 15/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1562 - loss: 2.4647
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1831 - loss: 2.3397 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1865 - loss: 2.3267
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1879 - loss: 2.3229
[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1894 - loss: 2.3195
[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1913 - loss: 2.3163
[1m220/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1923 - loss: 2.3140
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1933 - loss: 2.3115
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1942 - loss: 2.3092 - val_accuracy: 0.2611 - val_loss: 1.9958
Epoch 16/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1983 - loss: 2.3020 
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[1m147/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2015 - loss: 2.2847
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2026 - loss: 2.2800
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2031 - loss: 2.2763
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2037 - loss: 2.2713 - val_accuracy: 0.2666 - val_loss: 1.9833
Epoch 17/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2812 - loss: 2.1577
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2067 - loss: 2.2414 
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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2073 - loss: 2.2356
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2085 - loss: 2.2349
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2096 - loss: 2.2335 - val_accuracy: 0.2964 - val_loss: 1.9505
Epoch 18/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2031 - loss: 2.2779
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2221 - loss: 2.1975 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2196 - loss: 2.1965
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2186 - loss: 2.1985
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2179 - loss: 2.2016
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2179 - loss: 2.2015
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2183 - loss: 2.2011
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2188 - loss: 2.2004
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2189 - loss: 2.1999 - val_accuracy: 0.2683 - val_loss: 1.9384
Epoch 19/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 24ms/step - accuracy: 0.2656 - loss: 2.0013
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2214 - loss: 2.1553 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2256 - loss: 2.1588
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2274 - loss: 2.1591
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2275 - loss: 2.1618
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2271 - loss: 2.1642
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2264 - loss: 2.1659
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2259 - loss: 2.1669
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2255 - loss: 2.1676 - val_accuracy: 0.2823 - val_loss: 1.9205
Epoch 20/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2031 - loss: 2.2585
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2237 - loss: 2.1558 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2225 - loss: 2.1647
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2240 - loss: 2.1671
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2249 - loss: 2.1661
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2254 - loss: 2.1654
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2259 - loss: 2.1644
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2261 - loss: 2.1635
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2263 - loss: 2.1627 - val_accuracy: 0.3073 - val_loss: 1.8931
Epoch 21/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 27ms/step - accuracy: 0.2656 - loss: 2.1620
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2189 - loss: 2.1952 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2226 - loss: 2.1743
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2252 - loss: 2.1615
[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2266 - loss: 2.1531
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2277 - loss: 2.1471
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2287 - loss: 2.1432
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2292 - loss: 2.1407
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Epoch 22/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2376 - loss: 2.1242 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2357 - loss: 2.1244
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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2379 - loss: 2.1194
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2378 - loss: 2.1191 - val_accuracy: 0.2966 - val_loss: 1.8672
Epoch 23/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2344 - loss: 2.1154
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2321 - loss: 2.0906 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2355 - loss: 2.0943
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[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2374 - loss: 2.0967
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2374 - loss: 2.0961
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2375 - loss: 2.0956
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2377 - loss: 2.0953 - val_accuracy: 0.2910 - val_loss: 1.8493
Epoch 24/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2344 - loss: 2.0945
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2425 - loss: 2.0963 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2422 - loss: 2.0948
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[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2433 - loss: 2.0930
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2442 - loss: 2.0922
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2450 - loss: 2.0910
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2455 - loss: 2.0898
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2459 - loss: 2.0890 - val_accuracy: 0.3047 - val_loss: 1.8420
Epoch 25/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2969 - loss: 2.0712
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2372 - loss: 2.1029 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2361 - loss: 2.0967
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2368 - loss: 2.0945
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2379 - loss: 2.0921
[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2391 - loss: 2.0886
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2405 - loss: 2.0848
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2415 - loss: 2.0827
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2421 - loss: 2.0815 - val_accuracy: 0.3162 - val_loss: 1.8278
Epoch 26/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2031 - loss: 2.0666
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2443 - loss: 2.0804 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2429 - loss: 2.0783
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2441 - loss: 2.0738
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2450 - loss: 2.0721
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2455 - loss: 2.0707
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2463 - loss: 2.0687
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2470 - loss: 2.0672
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2473 - loss: 2.0664 - val_accuracy: 0.3217 - val_loss: 1.8191
Epoch 27/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2031 - loss: 2.1583
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2482 - loss: 2.0471 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2506 - loss: 2.0472
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[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2499 - loss: 2.0530
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2499 - loss: 2.0540
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2500 - loss: 2.0541
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2502 - loss: 2.0539 - val_accuracy: 0.3219 - val_loss: 1.8086
Epoch 28/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2718 - loss: 2.0303 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2696 - loss: 2.0326
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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2630 - loss: 2.0325
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2627 - loss: 2.0322
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Epoch 29/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3281 - loss: 1.8778
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2601 - loss: 2.0166 
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[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2627 - loss: 2.0213
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2625 - loss: 2.0212
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2629 - loss: 2.0214
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2630 - loss: 2.0213 - val_accuracy: 0.3290 - val_loss: 1.7977
Epoch 30/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3281 - loss: 1.9388
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2699 - loss: 2.0195 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2633 - loss: 2.0242
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[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2610 - loss: 2.0247
[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2610 - loss: 2.0246
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2616 - loss: 2.0240
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2624 - loss: 2.0228
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2629 - loss: 2.0219 - val_accuracy: 0.3241 - val_loss: 1.7866
Epoch 31/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.1719 - loss: 1.9961
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2410 - loss: 1.9984 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2459 - loss: 2.0129
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2491 - loss: 2.0131
[1m145/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2514 - loss: 2.0133
[1m182/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2533 - loss: 2.0119
[1m216/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2547 - loss: 2.0113
[1m251/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2559 - loss: 2.0110
[1m289/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2572 - loss: 2.0102
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2574 - loss: 2.0101 - val_accuracy: 0.3310 - val_loss: 1.7758
Epoch 32/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2344 - loss: 1.9965
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2651 - loss: 2.0166 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2725 - loss: 2.0052
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2745 - loss: 1.9991
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2752 - loss: 1.9960
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2756 - loss: 1.9939
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2762 - loss: 1.9916
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Epoch 33/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3010 - loss: 1.9745 
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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2871 - loss: 1.9764
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Epoch 34/110

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[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2744 - loss: 1.9644
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Epoch 35/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2912 - loss: 1.9291 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2855 - loss: 1.9370
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[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2845 - loss: 1.9460
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2847 - loss: 1.9479
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2850 - loss: 1.9514 - val_accuracy: 0.3186 - val_loss: 1.7527
Epoch 36/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2879 - loss: 1.9540 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2879 - loss: 1.9546
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[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2904 - loss: 1.9509
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2911 - loss: 1.9506
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2910 - loss: 1.9509
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2909 - loss: 1.9511 - val_accuracy: 0.3364 - val_loss: 1.7297
Epoch 37/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2585 - loss: 1.9655 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2646 - loss: 1.9591
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2710 - loss: 1.9555
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2799 - loss: 1.9526
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2811 - loss: 1.9507
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Epoch 38/110

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Epoch 39/110

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Epoch 40/110

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Epoch 41/110

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[1m245/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2978 - loss: 1.9006
[1m283/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2979 - loss: 1.9015
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Epoch 42/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2741 - loss: 1.9362 
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Epoch 43/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3066 - loss: 1.8979 
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Epoch 44/110

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Epoch 45/110

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Epoch 46/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3125 - loss: 1.8699 
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[1m219/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3033 - loss: 1.8688
[1m257/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3038 - loss: 1.8688
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.3042 - loss: 1.8688
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3042 - loss: 1.8688 - val_accuracy: 0.3497 - val_loss: 1.6791
Epoch 47/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3265 - loss: 1.8604 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3156 - loss: 1.8782
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3115 - loss: 1.8835
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3097 - loss: 1.8851
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3088 - loss: 1.8850
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3083 - loss: 1.8841
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3082 - loss: 1.8831
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Epoch 48/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3230 - loss: 1.8517 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3165 - loss: 1.8616
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Epoch 49/110

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Epoch 50/110

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Epoch 51/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3223 - loss: 1.8235 
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[1m218/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3223 - loss: 1.8326
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3211 - loss: 1.8362 - val_accuracy: 0.3560 - val_loss: 1.6520
Epoch 52/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4062 - loss: 1.7318
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3293 - loss: 1.8133 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3267 - loss: 1.8180
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[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3257 - loss: 1.8261
[1m199/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3253 - loss: 1.8287
[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3251 - loss: 1.8303
[1m277/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3248 - loss: 1.8314
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3248 - loss: 1.8318 - val_accuracy: 0.3526 - val_loss: 1.6515
Epoch 53/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3478 - loss: 1.8412 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3348 - loss: 1.8467
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3265 - loss: 1.8416
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Epoch 54/110

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Epoch 55/110

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Epoch 56/110

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Epoch 57/110

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Epoch 58/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3381 - loss: 1.8090 
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Epoch 59/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3300 - loss: 1.8052 
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Epoch 60/110

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Epoch 61/110

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Epoch 62/110

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Epoch 63/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3485 - loss: 1.7900 
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Epoch 64/110

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Epoch 65/110

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Epoch 66/110

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Epoch 67/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3393 - loss: 1.7786 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3416 - loss: 1.7743
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3395 - loss: 1.7702
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3388 - loss: 1.7705 - val_accuracy: 0.3652 - val_loss: 1.6207
Epoch 68/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3437 - loss: 1.7911 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3381 - loss: 1.7931
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[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3390 - loss: 1.7852
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3390 - loss: 1.7842
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3390 - loss: 1.7836
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3390 - loss: 1.7833 - val_accuracy: 0.3639 - val_loss: 1.6104
Epoch 69/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3394 - loss: 1.8114 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3335 - loss: 1.8113
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3347 - loss: 1.7945
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Epoch 70/110

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Epoch 71/110

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Epoch 72/110

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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3358 - loss: 1.7633 - val_accuracy: 0.3643 - val_loss: 1.6215
Epoch 73/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3364 - loss: 1.7906 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3449 - loss: 1.7661
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[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3457 - loss: 1.7610
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3454 - loss: 1.7606
[1m263/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3453 - loss: 1.7600
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3452 - loss: 1.7598 - val_accuracy: 0.3600 - val_loss: 1.6072
Epoch 74/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3256 - loss: 1.8466 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3319 - loss: 1.8228
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[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3363 - loss: 1.8027
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3371 - loss: 1.7992
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3377 - loss: 1.7963
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3387 - loss: 1.7912 - val_accuracy: 0.3587 - val_loss: 1.6164
Epoch 75/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3517 - loss: 1.7461 
[1m 70/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3473 - loss: 1.7536
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3422 - loss: 1.7543 - val_accuracy: 0.3665 - val_loss: 1.6133
Epoch 76/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2344 - loss: 1.9181
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3268 - loss: 1.8118 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3325 - loss: 1.7857
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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3374 - loss: 1.7670
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3391 - loss: 1.7625
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3399 - loss: 1.7611 - val_accuracy: 0.3623 - val_loss: 1.6056
Epoch 77/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3750 - loss: 1.9069
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3509 - loss: 1.7906 
[1m 68/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3439 - loss: 1.7753
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[1m140/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3424 - loss: 1.7622
[1m178/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3428 - loss: 1.7585
[1m215/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3435 - loss: 1.7555
[1m255/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3438 - loss: 1.7540
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3438 - loss: 1.7531 - val_accuracy: 0.3726 - val_loss: 1.6069
Epoch 78/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3594 - loss: 1.7361
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3313 - loss: 1.7765 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3344 - loss: 1.7627
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3351 - loss: 1.7570
[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3356 - loss: 1.7541
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3365 - loss: 1.7521
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3374 - loss: 1.7509
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3380 - loss: 1.7502
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3383 - loss: 1.7497 - val_accuracy: 0.3674 - val_loss: 1.6057
Epoch 79/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3438 - loss: 1.6715
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3572 - loss: 1.7077 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3510 - loss: 1.7292
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3506 - loss: 1.7334
[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3514 - loss: 1.7360
[1m197/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3518 - loss: 1.7364
[1m237/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3521 - loss: 1.7364
[1m277/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3520 - loss: 1.7367
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3519 - loss: 1.7369 - val_accuracy: 0.3671 - val_loss: 1.6038
Epoch 80/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3438 - loss: 1.7070
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3357 - loss: 1.7350 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3372 - loss: 1.7423
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3367 - loss: 1.7520
[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3372 - loss: 1.7544
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3382 - loss: 1.7545
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3391 - loss: 1.7546
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3400 - loss: 1.7544
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3404 - loss: 1.7542 - val_accuracy: 0.3621 - val_loss: 1.6172
Epoch 81/110

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Epoch 82/110

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Epoch 83/110

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Epoch 84/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3579 - loss: 1.7044 
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Epoch 85/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3517 - loss: 1.7632 
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Epoch 86/110

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Epoch 87/110

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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:12[0m 849ms/step
[1m63/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 812us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
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Saved model to disk.

Accuracy capturado en la ejecución 25: 35.51 [%]
F1-score capturado en la ejecución 25: 33.73 [%]

=== EJECUCIÓN 26 ===

--- TRAIN (ejecución 26) ---

--- TEST (ejecución 26) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18676, 6, 250)

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[1m 60/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 857us/step  
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m69/86[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 740us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 8ms/step

[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 63/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 809us/step
[1m134/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 755us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 36.48 [%]
Global F1 score (validation) = 35.51 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[2.2337675e-01 1.7693979e-01 1.9426140e-01 ... 1.7661985e-06
  1.6687672e-01 5.1340505e-02]
 [2.1308440e-01 1.9511299e-01 2.0569104e-01 ... 4.6728146e-06
  1.6186577e-01 1.3816273e-02]
 [2.1951362e-01 1.7382902e-01 2.0464745e-01 ... 2.5224390e-06
  1.6162327e-01 4.4686820e-02]
 ...
 [2.0616645e-01 2.0097253e-01 2.0252742e-01 ... 2.0538322e-05
  1.6182965e-01 1.3729143e-02]
 [1.9321127e-01 2.0354114e-01 1.9962168e-01 ... 8.2538987e-05
  1.7181240e-01 1.2388691e-02]
 [4.9601156e-02 6.9979087e-02 5.5290204e-02 ... 1.0580744e-03
  7.0642374e-02 1.9076497e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 43.26 [%]
Global accuracy score (test) = 35.51 [%]
Global F1 score (train) = 40.11 [%]
Global F1 score (test) = 33.05 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.23      0.55      0.32       184
 CAMINAR CON MÓVIL O LIBRO       0.21      0.29      0.24       184
       CAMINAR USUAL SPEED       0.14      0.02      0.04       184
            CAMINAR ZIGZAG       0.17      0.06      0.09       184
          DE PIE BARRIENDO       0.22      0.48      0.31       184
   DE PIE DOBLANDO TOALLAS       0.41      0.06      0.10       184
    DE PIE MOVIENDO LIBROS       0.25      0.39      0.31       184
          DE PIE USANDO PC       0.39      0.57      0.46       184
        FASE REPOSO CON K5       0.75      0.82      0.78       184
INCREMENTAL CICLOERGOMETRO       0.97      0.61      0.75       184
           SENTADO LEYENDO       0.34      0.52      0.41       184
         SENTADO USANDO PC       0.19      0.06      0.09       184
      SENTADO VIENDO LA TV       0.17      0.10      0.13       184
   SUBIR Y BAJAR ESCALERAS       0.31      0.08      0.13       184
                    TROTAR       0.84      0.77      0.80       161

                  accuracy                           0.36      2737
                 macro avg       0.37      0.36      0.33      2737
              weighted avg       0.37      0.36      0.33      2737

2025-10-28 14:30:43.964128: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 14:30:43.975641: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1761658243.989056 2425668 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761658243.993380 2425668 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761658244.003489 2425668 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761658244.003512 2425668 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761658244.003515 2425668 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761658244.003517 2425668 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 14:30:44.006883: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1761658246.377290 2425668 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13733 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/110
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761658248.088896 2425768 service.cc:152] XLA service 0x7e673000b6e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761658248.088956 2425768 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 14:30:48.129395: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
I0000 00:00:1761658248.302288 2425768 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761658250.554136 2425768 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/110

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Epoch 3/110

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[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1214 - loss: 3.0529
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[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1207 - loss: 3.0433
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1205 - loss: 3.0354 - val_accuracy: 0.1911 - val_loss: 2.3935
Epoch 4/110

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[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1204 - loss: 2.9395
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1208 - loss: 2.9358
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1216 - loss: 2.9319
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1219 - loss: 2.9291
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1220 - loss: 2.9275 - val_accuracy: 0.2246 - val_loss: 2.3629
Epoch 5/110

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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1346 - loss: 2.8381
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1345 - loss: 2.8344
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1346 - loss: 2.8325 - val_accuracy: 0.2226 - val_loss: 2.3197
Epoch 6/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1430 - loss: 2.7349 
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[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1425 - loss: 2.7402
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1427 - loss: 2.7383 - val_accuracy: 0.2344 - val_loss: 2.2923
Epoch 7/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1394 - loss: 2.7037 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1423 - loss: 2.6932
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[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1446 - loss: 2.6833
[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1450 - loss: 2.6803
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1455 - loss: 2.6771
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1466 - loss: 2.6717 - val_accuracy: 0.2287 - val_loss: 2.2613
Epoch 8/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2656 - loss: 2.4797
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1744 - loss: 2.5765 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1647 - loss: 2.5984
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[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1583 - loss: 2.6090
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1572 - loss: 2.6095
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1568 - loss: 2.6084
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1568 - loss: 2.6067
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1567 - loss: 2.6058 - val_accuracy: 0.2305 - val_loss: 2.2203
Epoch 9/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2188 - loss: 2.4257
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1535 - loss: 2.5863 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1562 - loss: 2.5764
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1576 - loss: 2.5703
[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1592 - loss: 2.5645
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1605 - loss: 2.5599
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1618 - loss: 2.5558
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1626 - loss: 2.5529
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1631 - loss: 2.5510 - val_accuracy: 0.2372 - val_loss: 2.1926
Epoch 10/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2344 - loss: 2.4358
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1813 - loss: 2.4893 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1778 - loss: 2.4928
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1777 - loss: 2.4920
[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1779 - loss: 2.4895
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1780 - loss: 2.4872
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1783 - loss: 2.4855
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1784 - loss: 2.4839
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Epoch 11/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 26ms/step - accuracy: 0.2188 - loss: 2.3939
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1694 - loss: 2.4509 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1706 - loss: 2.4416
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Epoch 12/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2188 - loss: 2.2805
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1934 - loss: 2.3832 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1897 - loss: 2.3890
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[1m144/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1902 - loss: 2.3909
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[1m214/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1909 - loss: 2.3901
[1m253/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1909 - loss: 2.3895
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.1907 - loss: 2.3890
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1907 - loss: 2.3890 - val_accuracy: 0.2666 - val_loss: 2.0719
Epoch 13/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1719 - loss: 2.4121
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1816 - loss: 2.3731 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1854 - loss: 2.3668
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1888 - loss: 2.3576
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1907 - loss: 2.3533
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1920 - loss: 2.3501
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1925 - loss: 2.3486
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1928 - loss: 2.3476
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1929 - loss: 2.3470 - val_accuracy: 0.2696 - val_loss: 2.0494
Epoch 14/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.1719 - loss: 2.3554
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1962 - loss: 2.3333 
[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2006 - loss: 2.3289
[1m119/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2029 - loss: 2.3236
[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2033 - loss: 2.3224
[1m197/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2039 - loss: 2.3211
[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2038 - loss: 2.3201
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2036 - loss: 2.3189
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2035 - loss: 2.3182 - val_accuracy: 0.2788 - val_loss: 2.0182
Epoch 15/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.2500 - loss: 2.2345
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2330 - loss: 2.2470 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2266 - loss: 2.2486
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2225 - loss: 2.2523
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2197 - loss: 2.2560
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2180 - loss: 2.2578
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2168 - loss: 2.2593
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2156 - loss: 2.2607
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2149 - loss: 2.2615 - val_accuracy: 0.2779 - val_loss: 1.9916
Epoch 16/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 26ms/step - accuracy: 0.0781 - loss: 2.3692
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1924 - loss: 2.2682 
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Epoch 17/110

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Epoch 18/110

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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2252 - loss: 2.1830
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2246 - loss: 2.1842 - val_accuracy: 0.2838 - val_loss: 1.9299
Epoch 19/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2351 - loss: 2.1689 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2312 - loss: 2.1727
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[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2273 - loss: 2.1762
[1m219/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2274 - loss: 2.1752
[1m258/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2276 - loss: 2.1741
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2278 - loss: 2.1729 - val_accuracy: 0.2929 - val_loss: 1.9037
Epoch 20/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2297 - loss: 2.1719 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2301 - loss: 2.1571
[1m119/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2300 - loss: 2.1537
[1m160/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2303 - loss: 2.1542
[1m199/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2309 - loss: 2.1537
[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2311 - loss: 2.1529
[1m276/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2313 - loss: 2.1519
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2313 - loss: 2.1515 - val_accuracy: 0.3005 - val_loss: 1.8941
Epoch 21/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2404 - loss: 2.1276 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2342 - loss: 2.1328
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2337 - loss: 2.1342 - val_accuracy: 0.2886 - val_loss: 1.8829
Epoch 22/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3125 - loss: 1.9312
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2661 - loss: 2.0936 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2587 - loss: 2.1021
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2547 - loss: 2.1051
[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2522 - loss: 2.1062
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2497 - loss: 2.1083
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2482 - loss: 2.1097
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2468 - loss: 2.1103 - val_accuracy: 0.3114 - val_loss: 1.8662
Epoch 23/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2500 - loss: 2.1085
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2252 - loss: 2.1538 
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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2364 - loss: 2.1169
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2374 - loss: 2.1140
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2383 - loss: 2.1117
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2390 - loss: 2.1093
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2395 - loss: 2.1078 - val_accuracy: 0.3053 - val_loss: 1.8482
Epoch 24/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.2812 - loss: 2.2441
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2388 - loss: 2.1176 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2439 - loss: 2.1066
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2445 - loss: 2.1016
[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2448 - loss: 2.0970
[1m197/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2448 - loss: 2.0939
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2451 - loss: 2.0914
[1m275/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2455 - loss: 2.0888
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2457 - loss: 2.0878 - val_accuracy: 0.2988 - val_loss: 1.8355
Epoch 25/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3281 - loss: 1.8773
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2637 - loss: 2.0471 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2605 - loss: 2.0545
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2601 - loss: 2.0555
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2591 - loss: 2.0545
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2584 - loss: 2.0548
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2579 - loss: 2.0552
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2574 - loss: 2.0557
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2573 - loss: 2.0559 - val_accuracy: 0.3056 - val_loss: 1.8335
Epoch 26/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2500 - loss: 2.0518
[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2560 - loss: 2.0655 
[1m 83/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2565 - loss: 2.0610
[1m120/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2569 - loss: 2.0595
[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2574 - loss: 2.0589
[1m198/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2578 - loss: 2.0581
[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2577 - loss: 2.0578
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2575 - loss: 2.0578
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2573 - loss: 2.0576 - val_accuracy: 0.3149 - val_loss: 1.8141
Epoch 27/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2656 - loss: 1.8732
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2605 - loss: 2.0080 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2584 - loss: 2.0213
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[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2570 - loss: 2.0307
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2580 - loss: 2.0310
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[1m259/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2595 - loss: 2.0312
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Epoch 28/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2632 - loss: 2.0213 
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[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2695 - loss: 2.0126
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2691 - loss: 2.0138 - val_accuracy: 0.3116 - val_loss: 1.8032
Epoch 29/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2500 - loss: 1.9977
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2542 - loss: 2.0010 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2563 - loss: 2.0139
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[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2605 - loss: 2.0183
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2616 - loss: 2.0177
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2623 - loss: 2.0170
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2630 - loss: 2.0162
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2635 - loss: 2.0154 - val_accuracy: 0.3158 - val_loss: 1.7853
Epoch 30/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3594 - loss: 1.6679
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2734 - loss: 1.9813 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2733 - loss: 1.9954
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2726 - loss: 1.9976
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2728 - loss: 1.9966
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2725 - loss: 1.9971
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2725 - loss: 1.9974
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2725 - loss: 1.9981
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2725 - loss: 1.9984 - val_accuracy: 0.3204 - val_loss: 1.7790
Epoch 31/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2500 - loss: 2.1407
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2531 - loss: 2.0230 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2550 - loss: 2.0225
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2584 - loss: 2.0164
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2616 - loss: 2.0107
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2640 - loss: 2.0067
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2658 - loss: 2.0037
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2672 - loss: 2.0017
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2679 - loss: 2.0007 - val_accuracy: 0.3406 - val_loss: 1.7624
Epoch 32/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2581 - loss: 1.9990 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2637 - loss: 1.9982
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[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2698 - loss: 1.9911
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Epoch 33/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2937 - loss: 1.9685 
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Epoch 34/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2785 - loss: 1.9699 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2820 - loss: 1.9646
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[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2842 - loss: 1.9641
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Epoch 35/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3141 - loss: 1.9729 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3068 - loss: 1.9636
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[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2982 - loss: 1.9563
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2968 - loss: 1.9551
[1m263/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2958 - loss: 1.9542
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2952 - loss: 1.9535 - val_accuracy: 0.3423 - val_loss: 1.7339
Epoch 36/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2767 - loss: 1.9740 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2832 - loss: 1.9671
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2853 - loss: 1.9588
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2862 - loss: 1.9560
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2870 - loss: 1.9542
[1m263/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2876 - loss: 1.9528
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2879 - loss: 1.9516 - val_accuracy: 0.3377 - val_loss: 1.7193
Epoch 37/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2861 - loss: 1.9467 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2854 - loss: 1.9472
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[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2882 - loss: 1.9425
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2885 - loss: 1.9414
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Epoch 38/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2930 - loss: 1.9383 
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Epoch 39/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3108 - loss: 1.9096 
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Epoch 40/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2962 - loss: 1.9501 
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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3002 - loss: 1.9199
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Epoch 41/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3300 - loss: 1.9183 
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[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3139 - loss: 1.9038
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3133 - loss: 1.9031
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3130 - loss: 1.9031
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3129 - loss: 1.9033 - val_accuracy: 0.3506 - val_loss: 1.7022
Epoch 42/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3064 - loss: 1.9015 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3162 - loss: 1.8979
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[1m181/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3152 - loss: 1.8940
[1m216/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3140 - loss: 1.8937
[1m253/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3132 - loss: 1.8938
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Epoch 43/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2911 - loss: 1.9074 
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Epoch 44/110

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Epoch 45/110

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[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3208 - loss: 1.8632
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Epoch 46/110

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[1m 33/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3412 - loss: 1.8414 
[1m 69/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3296 - loss: 1.8617
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[1m217/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3205 - loss: 1.8673
[1m257/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3199 - loss: 1.8670
[1m288/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3196 - loss: 1.8671
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3195 - loss: 1.8671 - val_accuracy: 0.3502 - val_loss: 1.6734
Epoch 47/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2979 - loss: 1.8445 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3043 - loss: 1.8527
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3083 - loss: 1.8533
[1m147/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3111 - loss: 1.8528
[1m183/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3126 - loss: 1.8523
[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3134 - loss: 1.8522
[1m258/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3139 - loss: 1.8529
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.3142 - loss: 1.8538
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Epoch 48/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3201 - loss: 1.8319 
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[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3173 - loss: 1.8516
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Epoch 49/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2990 - loss: 1.8599 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3058 - loss: 1.8570
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Epoch 50/110

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[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3120 - loss: 1.8682
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Epoch 51/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3515 - loss: 1.7871 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3392 - loss: 1.8155
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[1m220/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3269 - loss: 1.8361
[1m259/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3258 - loss: 1.8377
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3252 - loss: 1.8385 - val_accuracy: 0.3543 - val_loss: 1.6448
Epoch 52/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.2969 - loss: 1.8540
[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3121 - loss: 1.8609 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3161 - loss: 1.8424
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[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3213 - loss: 1.8323
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3222 - loss: 1.8317
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3226 - loss: 1.8308
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3227 - loss: 1.8306
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3227 - loss: 1.8307 - val_accuracy: 0.3515 - val_loss: 1.6514
Epoch 53/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3165 - loss: 1.8618 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3208 - loss: 1.8524
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3239 - loss: 1.8369
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3237 - loss: 1.8367
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Epoch 54/110

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Epoch 55/110

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Epoch 56/110

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Epoch 57/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3423 - loss: 1.7713 
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[1m218/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3331 - loss: 1.8028
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3321 - loss: 1.8046 - val_accuracy: 0.3454 - val_loss: 1.6351
Epoch 58/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3266 - loss: 1.8484 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3284 - loss: 1.8404
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[1m220/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3334 - loss: 1.8245
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Epoch 59/110

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Epoch 60/110

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Epoch 61/110

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Epoch 62/110

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[1m254/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3390 - loss: 1.7832
[1m289/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3384 - loss: 1.7836
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3384 - loss: 1.7837 - val_accuracy: 0.3547 - val_loss: 1.6258
Epoch 63/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3432 - loss: 1.7410 
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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3327 - loss: 1.7777
[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3318 - loss: 1.7827
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3316 - loss: 1.7853
[1m263/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3316 - loss: 1.7871
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Epoch 64/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3341 - loss: 1.8028 
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3379 - loss: 1.7931
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Epoch 65/110

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Epoch 66/110

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Epoch 67/110

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Epoch 68/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3549 - loss: 1.7792 
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[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3461 - loss: 1.7623
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Epoch 69/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3337 - loss: 1.7849 
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[1m219/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3402 - loss: 1.7668
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Epoch 70/110

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Epoch 71/110

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Epoch 72/110

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[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3292 - loss: 1.7442
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[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3356 - loss: 1.7474
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3367 - loss: 1.7492 - val_accuracy: 0.3521 - val_loss: 1.6120
Epoch 73/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3438 - loss: 1.5798
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3389 - loss: 1.7242 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3376 - loss: 1.7376
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[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3400 - loss: 1.7447
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3405 - loss: 1.7461
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3407 - loss: 1.7474
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3409 - loss: 1.7482 - val_accuracy: 0.3549 - val_loss: 1.6074
Epoch 74/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 27ms/step - accuracy: 0.4688 - loss: 1.5101
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3613 - loss: 1.7314 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3570 - loss: 1.7356
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3547 - loss: 1.7415
[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3526 - loss: 1.7483
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3518 - loss: 1.7510
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3510 - loss: 1.7528
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3507 - loss: 1.7530
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3507 - loss: 1.7528 - val_accuracy: 0.3619 - val_loss: 1.6073
Epoch 75/110

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[1m 34/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3401 - loss: 1.7311 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3432 - loss: 1.7436
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3440 - loss: 1.7456
[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3437 - loss: 1.7471
[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3436 - loss: 1.7489
[1m220/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3433 - loss: 1.7502
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3433 - loss: 1.7513
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Epoch 76/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3486 - loss: 1.7113 
[1m 70/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3536 - loss: 1.7133
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Epoch 77/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3622 - loss: 1.7705 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3556 - loss: 1.7544
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[1m144/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3529 - loss: 1.7512
[1m183/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3522 - loss: 1.7514
[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3522 - loss: 1.7512
[1m259/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3518 - loss: 1.7514
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3516 - loss: 1.7515 - val_accuracy: 0.3671 - val_loss: 1.5948
Epoch 78/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3546 - loss: 1.7349 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3532 - loss: 1.7353
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3525 - loss: 1.7349
[1m146/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3506 - loss: 1.7377
[1m182/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3492 - loss: 1.7397
[1m220/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3484 - loss: 1.7404
[1m255/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3481 - loss: 1.7404
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3476 - loss: 1.7411 - val_accuracy: 0.3567 - val_loss: 1.6108
Epoch 79/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3438 - loss: 1.6185
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3594 - loss: 1.7192 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3571 - loss: 1.7196
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3560 - loss: 1.7266
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3544 - loss: 1.7341
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3540 - loss: 1.7364
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3537 - loss: 1.7378
[1m263/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3533 - loss: 1.7384
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3530 - loss: 1.7386 - val_accuracy: 0.3549 - val_loss: 1.6071
Epoch 80/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3906 - loss: 1.7051
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3662 - loss: 1.6970 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3551 - loss: 1.7202
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3516 - loss: 1.7277
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3490 - loss: 1.7326
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3479 - loss: 1.7339
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3474 - loss: 1.7353
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3470 - loss: 1.7362
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3468 - loss: 1.7368 - val_accuracy: 0.3637 - val_loss: 1.6053
Epoch 81/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3271 - loss: 1.7271 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3321 - loss: 1.7337
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Epoch 82/110

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Saved model to disk.

Accuracy capturado en la ejecución 26: 35.51 [%]
F1-score capturado en la ejecución 26: 33.05 [%]

=== EJECUCIÓN 27 ===

--- TRAIN (ejecución 27) ---

--- TEST (ejecución 27) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18676, 6, 250)

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[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 67/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 763us/step
[1m143/169[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 710us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 35.6 [%]
Global F1 score (validation) = 35.37 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[1.99786425e-01 1.55054554e-01 1.75230116e-01 ... 4.79068649e-06
  1.72657445e-01 1.13528386e-01]
 [2.13103786e-01 2.02139705e-01 2.07536310e-01 ... 7.46751221e-06
  1.47441104e-01 7.70174060e-03]
 [2.09303275e-01 1.97068959e-01 2.01030314e-01 ... 2.11260813e-05
  1.60859793e-01 9.13783070e-03]
 ...
 [2.09044382e-01 1.75637946e-01 1.99485615e-01 ... 6.02865885e-06
  1.79541603e-01 3.83893736e-02]
 [1.75896585e-01 2.01080441e-01 2.06786990e-01 ... 1.44573365e-04
  1.97910011e-01 1.55268274e-02]
 [5.25218025e-02 7.49109313e-02 6.40470684e-02 ... 3.75089701e-03
  7.13500381e-02 2.17623077e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 44.31 [%]
Global accuracy score (test) = 36.24 [%]
Global F1 score (train) = 43.15 [%]
Global F1 score (test) = 35.07 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.22      0.46      0.30       184
 CAMINAR CON MÓVIL O LIBRO       0.15      0.16      0.16       184
       CAMINAR USUAL SPEED       0.12      0.03      0.05       184
            CAMINAR ZIGZAG       0.18      0.19      0.18       184
          DE PIE BARRIENDO       0.27      0.49      0.34       184
   DE PIE DOBLANDO TOALLAS       0.25      0.12      0.17       184
    DE PIE MOVIENDO LIBROS       0.27      0.26      0.27       184
          DE PIE USANDO PC       0.45      0.60      0.52       184
        FASE REPOSO CON K5       0.64      0.78      0.70       184
INCREMENTAL CICLOERGOMETRO       0.79      0.61      0.69       184
           SENTADO LEYENDO       0.30      0.33      0.32       184
         SENTADO USANDO PC       0.51      0.10      0.16       184
      SENTADO VIENDO LA TV       0.35      0.42      0.38       184
   SUBIR Y BAJAR ESCALERAS       0.36      0.16      0.22       184
                    TROTAR       0.85      0.76      0.80       161

                  accuracy                           0.36      2737
                 macro avg       0.38      0.37      0.35      2737
              weighted avg       0.38      0.36      0.35      2737

2025-10-28 14:31:53.046273: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 14:31:53.057611: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1761658313.070842 2434311 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761658313.075099 2434311 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761658313.084965 2434311 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761658313.084985 2434311 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761658313.084988 2434311 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761658313.084989 2434311 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 14:31:53.088270: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1761658315.472875 2434311 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13733 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/110
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761658317.162164 2434445 service.cc:152] XLA service 0x744aac002550 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761658317.162226 2434445 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 14:31:57.204377: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
I0000 00:00:1761658317.375506 2434445 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761658319.698719 2434445 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/110

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Epoch 3/110

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Epoch 4/110

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Epoch 5/110

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[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1279 - loss: 2.8390
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1283 - loss: 2.8350 - val_accuracy: 0.2070 - val_loss: 2.3479
Epoch 6/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1250 - loss: 2.7103
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1148 - loss: 2.8114 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1190 - loss: 2.8037
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1222 - loss: 2.7980
[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1245 - loss: 2.7936
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1262 - loss: 2.7898
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1271 - loss: 2.7873
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1286 - loss: 2.7822 - val_accuracy: 0.1993 - val_loss: 2.3205
Epoch 7/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2031 - loss: 2.7636
[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1505 - loss: 2.6769 
[1m 82/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1477 - loss: 2.6872
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[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1461 - loss: 2.6872
[1m197/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1460 - loss: 2.6871
[1m237/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1459 - loss: 2.6863
[1m275/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1461 - loss: 2.6851
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1462 - loss: 2.6845 - val_accuracy: 0.2220 - val_loss: 2.2844
Epoch 8/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1094 - loss: 2.7951
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1491 - loss: 2.6457 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1489 - loss: 2.6497
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1497 - loss: 2.6483
[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1496 - loss: 2.6473
[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1496 - loss: 2.6457
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1498 - loss: 2.6432
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1500 - loss: 2.6401
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1502 - loss: 2.6379 - val_accuracy: 0.2387 - val_loss: 2.2410
Epoch 9/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2031 - loss: 2.4349
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1729 - loss: 2.5566 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1676 - loss: 2.5579
[1m108/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1655 - loss: 2.5578
[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1645 - loss: 2.5581
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1636 - loss: 2.5584
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1632 - loss: 2.5578
[1m263/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1631 - loss: 2.5565
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1631 - loss: 2.5559 - val_accuracy: 0.2475 - val_loss: 2.1992
Epoch 10/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.0938 - loss: 2.6874
[1m 32/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1671 - loss: 2.5226 
[1m 69/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1680 - loss: 2.5193
[1m106/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1669 - loss: 2.5205
[1m144/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1667 - loss: 2.5196
[1m182/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1667 - loss: 2.5176
[1m219/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1672 - loss: 2.5150
[1m258/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1679 - loss: 2.5126
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Epoch 11/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1250 - loss: 2.5580
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1570 - loss: 2.5098 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1642 - loss: 2.4881
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[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1725 - loss: 2.4674
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1742 - loss: 2.4636
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Epoch 12/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1719 - loss: 2.3875
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1641 - loss: 2.4199 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1701 - loss: 2.4262
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1734 - loss: 2.4253
[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1754 - loss: 2.4223
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1777 - loss: 2.4161
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1785 - loss: 2.4136
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1789 - loss: 2.4123 - val_accuracy: 0.2470 - val_loss: 2.0899
Epoch 13/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1875 - loss: 2.4487
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1828 - loss: 2.3928 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1852 - loss: 2.3823
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[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1880 - loss: 2.3776
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1895 - loss: 2.3750
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1904 - loss: 2.3724
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1912 - loss: 2.3698
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1914 - loss: 2.3688 - val_accuracy: 0.2509 - val_loss: 2.0432
Epoch 14/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1406 - loss: 2.3995
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1997 - loss: 2.3275 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1999 - loss: 2.3293
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1985 - loss: 2.3344
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1988 - loss: 2.3353
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1990 - loss: 2.3344
[1m222/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1991 - loss: 2.3336
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1992 - loss: 2.3324
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1993 - loss: 2.3310 - val_accuracy: 0.2568 - val_loss: 2.0190
Epoch 15/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2031 - loss: 2.1715
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2016 - loss: 2.2731 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2034 - loss: 2.2830
[1m121/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2033 - loss: 2.2883
[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2034 - loss: 2.2886
[1m200/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2038 - loss: 2.2876
[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2040 - loss: 2.2869
[1m277/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2042 - loss: 2.2862
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2044 - loss: 2.2857 - val_accuracy: 0.2740 - val_loss: 1.9903
Epoch 16/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2031 - loss: 2.2984
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2008 - loss: 2.2762 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2059 - loss: 2.2625
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[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2066 - loss: 2.2610
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2068 - loss: 2.2605
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2074 - loss: 2.2592
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2086 - loss: 2.2563 - val_accuracy: 0.2699 - val_loss: 1.9610
Epoch 17/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1562 - loss: 2.2653
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1980 - loss: 2.2630 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2062 - loss: 2.2416
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[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2093 - loss: 2.2304
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[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2112 - loss: 2.2269
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2117 - loss: 2.2260 - val_accuracy: 0.2860 - val_loss: 1.9362
Epoch 18/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2031 - loss: 2.3128
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2170 - loss: 2.2333 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2155 - loss: 2.2207
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[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2173 - loss: 2.2086
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2181 - loss: 2.2057
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2185 - loss: 2.2038
[1m263/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2191 - loss: 2.2025
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2195 - loss: 2.2017 - val_accuracy: 0.2847 - val_loss: 1.9229
Epoch 19/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1875 - loss: 2.2097
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2145 - loss: 2.2078 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2185 - loss: 2.1965
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2205 - loss: 2.1919
[1m147/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2214 - loss: 2.1890
[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2218 - loss: 2.1870
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2225 - loss: 2.1844
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2230 - loss: 2.1827
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2234 - loss: 2.1817 - val_accuracy: 0.3034 - val_loss: 1.8978
Epoch 20/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2500 - loss: 2.1643
[1m 34/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2228 - loss: 2.1528 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2226 - loss: 2.1574
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2230 - loss: 2.1593
[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2238 - loss: 2.1587
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2249 - loss: 2.1578
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2258 - loss: 2.1561
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2262 - loss: 2.1552
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2262 - loss: 2.1549 - val_accuracy: 0.3042 - val_loss: 1.8861
Epoch 21/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2656 - loss: 2.0003
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2433 - loss: 2.1251 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2389 - loss: 2.1330
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2355 - loss: 2.1353
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2345 - loss: 2.1350
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2342 - loss: 2.1348
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2339 - loss: 2.1349
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2336 - loss: 2.1349
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Epoch 22/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.2188 - loss: 2.1262
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2354 - loss: 2.1499 
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Epoch 23/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2656 - loss: 2.0727
[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2501 - loss: 2.0812 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2461 - loss: 2.0866
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[1m242/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2451 - loss: 2.0937
[1m280/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2451 - loss: 2.0940
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2451 - loss: 2.0941 - val_accuracy: 0.3075 - val_loss: 1.8430
Epoch 24/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2969 - loss: 2.3088
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2411 - loss: 2.1254 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2424 - loss: 2.1127
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2416 - loss: 2.1087
[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2415 - loss: 2.1055
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2414 - loss: 2.1037
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2415 - loss: 2.1021
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2419 - loss: 2.1002
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2423 - loss: 2.0988 - val_accuracy: 0.3145 - val_loss: 1.8310
Epoch 25/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1406 - loss: 2.1268
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2344 - loss: 2.1041 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2371 - loss: 2.1022
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2386 - loss: 2.0996
[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2406 - loss: 2.0938
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2414 - loss: 2.0902
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2418 - loss: 2.0881
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2424 - loss: 2.0865
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2428 - loss: 2.0857 - val_accuracy: 0.3006 - val_loss: 1.8222
Epoch 26/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1875 - loss: 2.0374
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2358 - loss: 2.0769 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2418 - loss: 2.0723
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2444 - loss: 2.0674
[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2460 - loss: 2.0641
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2467 - loss: 2.0625
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2471 - loss: 2.0615
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2474 - loss: 2.0607
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2477 - loss: 2.0605 - val_accuracy: 0.3110 - val_loss: 1.8125
Epoch 27/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 27ms/step - accuracy: 0.3125 - loss: 2.0996
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2634 - loss: 2.0680 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2616 - loss: 2.0606
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2579 - loss: 2.0594
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2575 - loss: 2.0588
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2571 - loss: 2.0580
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2569 - loss: 2.0569
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2568 - loss: 2.0562 - val_accuracy: 0.3116 - val_loss: 1.8064
Epoch 28/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3281 - loss: 1.8769
[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2638 - loss: 1.9923 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2648 - loss: 2.0003
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2647 - loss: 2.0048
[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2640 - loss: 2.0104
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2628 - loss: 2.0152
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2619 - loss: 2.0186
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2614 - loss: 2.0211
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2612 - loss: 2.0222 - val_accuracy: 0.3093 - val_loss: 1.7913
Epoch 29/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1250 - loss: 1.9800
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2538 - loss: 2.0054 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2607 - loss: 2.0032
[1m113/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2612 - loss: 2.0072
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2610 - loss: 2.0100
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2602 - loss: 2.0122
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2599 - loss: 2.0135
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2597 - loss: 2.0144
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2596 - loss: 2.0148 - val_accuracy: 0.3297 - val_loss: 1.7824
Epoch 30/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2188 - loss: 2.0700
[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2572 - loss: 2.0082 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2579 - loss: 2.0170
[1m120/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2597 - loss: 2.0175
[1m160/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2610 - loss: 2.0180
[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2618 - loss: 2.0180
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2625 - loss: 2.0174
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2630 - loss: 2.0168
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2633 - loss: 2.0166 - val_accuracy: 0.3204 - val_loss: 1.7715
Epoch 31/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2031 - loss: 2.1920
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2624 - loss: 2.0180 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2654 - loss: 2.0098
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2652 - loss: 2.0062
[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2665 - loss: 2.0044
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2671 - loss: 2.0037
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2674 - loss: 2.0031
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2677 - loss: 2.0025
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2678 - loss: 2.0024 - val_accuracy: 0.3212 - val_loss: 1.7647
Epoch 32/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2812 - loss: 1.9109
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2551 - loss: 2.0043 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2637 - loss: 2.0041
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2664 - loss: 2.0030
[1m157/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2679 - loss: 2.0025
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2688 - loss: 2.0021
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2695 - loss: 2.0011
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2700 - loss: 1.9994
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Epoch 33/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2955 - loss: 1.9757 
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Epoch 34/110

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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2777 - loss: 1.9654
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Epoch 35/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2841 - loss: 1.9689 
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2855 - loss: 1.9649
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2851 - loss: 1.9653
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2850 - loss: 1.9653 - val_accuracy: 0.3395 - val_loss: 1.7298
Epoch 36/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2912 - loss: 1.9514 
[1m 82/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2897 - loss: 1.9545
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[1m162/292[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2882 - loss: 1.9565
[1m202/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2882 - loss: 1.9555
[1m243/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2879 - loss: 1.9562
[1m283/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2877 - loss: 1.9571
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2877 - loss: 1.9573 - val_accuracy: 0.3332 - val_loss: 1.7293
Epoch 37/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1719 - loss: 2.0292
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2794 - loss: 1.9465 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2836 - loss: 1.9501
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2897 - loss: 1.9462
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2899 - loss: 1.9456
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Epoch 38/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2886 - loss: 1.9149 
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Epoch 39/110

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Epoch 40/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2765 - loss: 1.9411 
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Epoch 41/110

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[1m 33/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3081 - loss: 1.8827 
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[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2999 - loss: 1.9107
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2997 - loss: 1.9111
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2996 - loss: 1.9115 - val_accuracy: 0.3495 - val_loss: 1.7044
Epoch 42/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2833 - loss: 1.9192 
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[1m145/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2898 - loss: 1.9095
[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2909 - loss: 1.9100
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2919 - loss: 1.9094
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2928 - loss: 1.9089
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Epoch 43/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2919 - loss: 1.9406 
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Epoch 44/110

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Epoch 45/110

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Epoch 46/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1719 - loss: 2.2166
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2886 - loss: 1.9241 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2960 - loss: 1.9084
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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3090 - loss: 1.8908
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3097 - loss: 1.8893
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3099 - loss: 1.8887 - val_accuracy: 0.3473 - val_loss: 1.6665
Epoch 47/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2969 - loss: 1.9451
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3008 - loss: 1.8881 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3064 - loss: 1.8786
[1m119/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3079 - loss: 1.8780
[1m160/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3083 - loss: 1.8782
[1m197/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3088 - loss: 1.8782
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3092 - loss: 1.8780
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3094 - loss: 1.8775
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3095 - loss: 1.8773 - val_accuracy: 0.3463 - val_loss: 1.6577
Epoch 48/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3594 - loss: 1.7081
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3099 - loss: 1.8636 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3085 - loss: 1.8766
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[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3110 - loss: 1.8742
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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3115 - loss: 1.8722
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3118 - loss: 1.8714
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Epoch 49/110

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Epoch 50/110

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Epoch 51/110

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Epoch 52/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3519 - loss: 1.8302 
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[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3342 - loss: 1.8314
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3315 - loss: 1.8340 - val_accuracy: 0.3569 - val_loss: 1.6524
Epoch 53/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3123 - loss: 1.8509 
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[1m218/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3179 - loss: 1.8527
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Epoch 54/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3186 - loss: 1.8575 
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Epoch 55/110

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Epoch 56/110

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Epoch 57/110

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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3332 - loss: 1.8272 - val_accuracy: 0.3589 - val_loss: 1.6245
Epoch 58/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3329 - loss: 1.7882 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3301 - loss: 1.7948
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[1m259/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3270 - loss: 1.8069
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Epoch 59/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3325 - loss: 1.8537 
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Epoch 60/110

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Epoch 61/110

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Epoch 62/110

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Epoch 63/110

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Epoch 64/110

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Epoch 65/110

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Epoch 66/110

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Epoch 67/110

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Epoch 68/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3417 - loss: 1.7596 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3392 - loss: 1.7684
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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3389 - loss: 1.7714
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3387 - loss: 1.7736 - val_accuracy: 0.3541 - val_loss: 1.6109
Epoch 69/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3362 - loss: 1.7253 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3319 - loss: 1.7458
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3287 - loss: 1.7696
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3290 - loss: 1.7731
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3300 - loss: 1.7745
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3307 - loss: 1.7758
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Epoch 70/110

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[1m 33/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3501 - loss: 1.7449 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3429 - loss: 1.7585
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3422 - loss: 1.7726
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Epoch 71/110

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[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3337 - loss: 1.7712
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Epoch 72/110

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Epoch 73/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3312 - loss: 1.7972 
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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3369 - loss: 1.7908
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3382 - loss: 1.7873 - val_accuracy: 0.3715 - val_loss: 1.6061
Epoch 74/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3256 - loss: 1.7518 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3273 - loss: 1.7549
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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3322 - loss: 1.7587
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3334 - loss: 1.7597
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3344 - loss: 1.7604
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3352 - loss: 1.7610
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3354 - loss: 1.7614 - val_accuracy: 0.3741 - val_loss: 1.5943
Epoch 75/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3418 - loss: 1.7953 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3429 - loss: 1.7722
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[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3426 - loss: 1.7627
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3424 - loss: 1.7628
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3424 - loss: 1.7626
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Epoch 76/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3444 - loss: 1.7777 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3448 - loss: 1.7701
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Epoch 77/110

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[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3342 - loss: 1.7606
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[1m239/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3389 - loss: 1.7582
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Epoch 78/110

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[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3414 - loss: 1.7425
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3447 - loss: 1.7513
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Epoch 79/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3341 - loss: 1.7751 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3387 - loss: 1.7664
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3408 - loss: 1.7601
[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3417 - loss: 1.7568
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3420 - loss: 1.7559
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3421 - loss: 1.7558
[1m262/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3422 - loss: 1.7554
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3423 - loss: 1.7549 - val_accuracy: 0.3584 - val_loss: 1.5948
Epoch 80/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3328 - loss: 1.7946 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3410 - loss: 1.7726
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3425 - loss: 1.7628
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3425 - loss: 1.7589
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3425 - loss: 1.7560
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3428 - loss: 1.7531
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3435 - loss: 1.7513
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3438 - loss: 1.7503 - val_accuracy: 0.3710 - val_loss: 1.5923
Epoch 81/110

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[1m 33/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3427 - loss: 1.7851 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3436 - loss: 1.7670
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[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3469 - loss: 1.7552
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3470 - loss: 1.7541
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3469 - loss: 1.7536
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3468 - loss: 1.7531
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Epoch 82/110

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Epoch 83/110

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Epoch 84/110

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Epoch 85/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3737 - loss: 1.7038 
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Epoch 86/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3528 - loss: 1.7538 
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Epoch 87/110

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Epoch 88/110

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[1m 82/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3651 - loss: 1.7086
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Epoch 89/110

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Epoch 90/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3495 - loss: 1.7486 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3516 - loss: 1.7381
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[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3540 - loss: 1.7313
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3542 - loss: 1.7304
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3544 - loss: 1.7292
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3543 - loss: 1.7289 - val_accuracy: 0.3739 - val_loss: 1.5845
Epoch 91/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3601 - loss: 1.7319 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3593 - loss: 1.7245
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[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3585 - loss: 1.7207
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3584 - loss: 1.7194
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3582 - loss: 1.7187
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3575 - loss: 1.7183 - val_accuracy: 0.3654 - val_loss: 1.5855
Epoch 92/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3338 - loss: 1.7498 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3340 - loss: 1.7423
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[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3409 - loss: 1.7310
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Epoch 93/110

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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3485 - loss: 1.7277 - val_accuracy: 0.3641 - val_loss: 1.5986

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:12[0m 848ms/step
[1m68/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 749us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
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Saved model to disk.

Accuracy capturado en la ejecución 27: 36.24 [%]
F1-score capturado en la ejecución 27: 35.07 [%]

=== EJECUCIÓN 28 ===

--- TRAIN (ejecución 28) ---

--- TEST (ejecución 28) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18676, 6, 250)

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:18[0m 855ms/step
[1m 68/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 755us/step  
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[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m65/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 787us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 65/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 791us/step
[1m133/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 763us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 36.41 [%]
Global F1 score (validation) = 36.53 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[2.1734515e-01 1.7787518e-01 2.1271560e-01 ... 1.4188341e-06
  1.5161985e-01 4.0131673e-02]
 [2.0057182e-01 2.2029482e-01 2.0786533e-01 ... 3.5977505e-06
  1.4501122e-01 4.8887110e-03]
 [1.5362968e-01 1.1301473e-01 1.5728812e-01 ... 2.6688533e-06
  1.6104928e-01 2.7644667e-01]
 ...
 [2.0086469e-01 2.1640086e-01 2.0884711e-01 ... 1.1560336e-05
  1.4957306e-01 6.8727802e-03]
 [1.8016414e-01 2.1508378e-01 1.9211873e-01 ... 8.3924155e-05
  1.6026247e-01 7.9326043e-03]
 [2.8968938e-02 4.4887088e-02 3.0385872e-02 ... 1.3446985e-03
  4.4972129e-02 1.8512632e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 44.95 [%]
Global accuracy score (test) = 35.77 [%]
Global F1 score (train) = 44.45 [%]
Global F1 score (test) = 35.38 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.26      0.10      0.15       184
 CAMINAR CON MÓVIL O LIBRO       0.20      0.20      0.20       184
       CAMINAR USUAL SPEED       0.19      0.30      0.23       184
            CAMINAR ZIGZAG       0.21      0.23      0.22       184
          DE PIE BARRIENDO       0.22      0.57      0.32       184
   DE PIE DOBLANDO TOALLAS       0.20      0.08      0.11       184
    DE PIE MOVIENDO LIBROS       0.26      0.28      0.27       184
          DE PIE USANDO PC       0.41      0.52      0.46       184
        FASE REPOSO CON K5       0.73      0.80      0.76       184
INCREMENTAL CICLOERGOMETRO       0.91      0.60      0.72       184
           SENTADO LEYENDO       0.34      0.46      0.39       184
         SENTADO USANDO PC       0.34      0.12      0.18       184
      SENTADO VIENDO LA TV       0.29      0.26      0.28       184
   SUBIR Y BAJAR ESCALERAS       0.46      0.17      0.25       184
                    TROTAR       0.83      0.73      0.77       161

                  accuracy                           0.36      2737
                 macro avg       0.39      0.36      0.35      2737
              weighted avg       0.39      0.36      0.35      2737

2025-10-28 14:33:07.837962: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 14:33:07.849344: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1761658387.862736 2444007 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761658387.866882 2444007 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761658387.876996 2444007 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761658387.877016 2444007 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761658387.877018 2444007 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761658387.877020 2444007 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 14:33:07.880284: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1761658390.285075 2444007 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13733 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/110
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761658392.048572 2444143 service.cc:152] XLA service 0x73981000cc10 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761658392.048633 2444143 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 14:33:12.087686: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
I0000 00:00:1761658392.251512 2444143 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761658394.516332 2444143 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/110

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Epoch 3/110

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[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1170 - loss: 3.0552
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Epoch 4/110

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[1m237/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1281 - loss: 2.9144
[1m274/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1277 - loss: 2.9133
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1276 - loss: 2.9127 - val_accuracy: 0.1859 - val_loss: 2.3412
Epoch 5/110

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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1322 - loss: 2.8405
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Epoch 6/110

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Epoch 7/110

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Epoch 8/110

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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1605 - loss: 2.6098 - val_accuracy: 0.2457 - val_loss: 2.1598
Epoch 9/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1637 - loss: 2.5339 
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[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1639 - loss: 2.5339
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1639 - loss: 2.5332
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1642 - loss: 2.5323
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1645 - loss: 2.5312 - val_accuracy: 0.2544 - val_loss: 2.1232
Epoch 10/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1643 - loss: 2.5443 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1668 - loss: 2.5291
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[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1692 - loss: 2.5135
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1701 - loss: 2.5092
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1711 - loss: 2.5046
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Epoch 11/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1746 - loss: 2.4361 
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Epoch 12/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2188 - loss: 2.4441
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Epoch 13/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2031 - loss: 2.3798
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1814 - loss: 2.3542 
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1940 - loss: 2.3369
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1941 - loss: 2.3362
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1941 - loss: 2.3358 - val_accuracy: 0.2601 - val_loss: 2.0076
Epoch 14/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.3281 - loss: 2.1884
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2201 - loss: 2.2902 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2139 - loss: 2.2907
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[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2084 - loss: 2.2934
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2069 - loss: 2.2934
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2060 - loss: 2.2934
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2053 - loss: 2.2930
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2051 - loss: 2.2928 - val_accuracy: 0.2623 - val_loss: 1.9657
Epoch 15/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 21ms/step - accuracy: 0.2969 - loss: 2.1398
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2288 - loss: 2.2282 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2206 - loss: 2.2412
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2168 - loss: 2.2502
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2145 - loss: 2.2562
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2127 - loss: 2.2595
[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2112 - loss: 2.2618
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2101 - loss: 2.2631
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2095 - loss: 2.2638 - val_accuracy: 0.2588 - val_loss: 1.9558
Epoch 16/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2812 - loss: 2.0657
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2263 - loss: 2.2316 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2250 - loss: 2.2425
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[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2208 - loss: 2.2485
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2192 - loss: 2.2478
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2182 - loss: 2.2467
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2171 - loss: 2.2457
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Epoch 17/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2149 - loss: 2.2376 
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Epoch 18/110

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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2188 - loss: 2.1800
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2193 - loss: 2.1797
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2195 - loss: 2.1796 - val_accuracy: 0.2751 - val_loss: 1.9150
Epoch 19/110

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[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2376 - loss: 2.1622
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[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2308 - loss: 2.1661
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2302 - loss: 2.1659
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2297 - loss: 2.1658
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2294 - loss: 2.1656 - val_accuracy: 0.2940 - val_loss: 1.8933
Epoch 20/110

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[1m 34/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2176 - loss: 2.1712 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2253 - loss: 2.1616
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[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2328 - loss: 2.1499
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2333 - loss: 2.1490
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2333 - loss: 2.1491
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2332 - loss: 2.1490
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2330 - loss: 2.1488 - val_accuracy: 0.2940 - val_loss: 1.8815
Epoch 21/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2522 - loss: 2.1146 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2461 - loss: 2.1268
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[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2397 - loss: 2.1322
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2379 - loss: 2.1334
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2369 - loss: 2.1342
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2366 - loss: 2.1340
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Epoch 22/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2519 - loss: 2.0833 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2490 - loss: 2.0809
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2420 - loss: 2.0953
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Epoch 23/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2389 - loss: 2.0985 
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2378 - loss: 2.0949
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Epoch 24/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3125 - loss: 1.9399
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2661 - loss: 2.0581 
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[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2508 - loss: 2.0797
[1m275/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2500 - loss: 2.0812
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2499 - loss: 2.0814 - val_accuracy: 0.3073 - val_loss: 1.8475
Epoch 25/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2188 - loss: 2.0899
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2486 - loss: 2.0641 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2505 - loss: 2.0604
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[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2518 - loss: 2.0630
[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2506 - loss: 2.0663
[1m221/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2496 - loss: 2.0684
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2490 - loss: 2.0697
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2489 - loss: 2.0701 - val_accuracy: 0.3114 - val_loss: 1.8303
Epoch 26/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2656 - loss: 1.9474
[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2609 - loss: 2.0465 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2619 - loss: 2.0485
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2607 - loss: 2.0502
[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2597 - loss: 2.0503
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2591 - loss: 2.0502
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2584 - loss: 2.0502
[1m276/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2577 - loss: 2.0507
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2575 - loss: 2.0509 - val_accuracy: 0.3158 - val_loss: 1.8299
Epoch 27/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2500 - loss: 2.0301
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2482 - loss: 2.0336 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2542 - loss: 2.0320
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[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2574 - loss: 2.0339
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2586 - loss: 2.0348
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2591 - loss: 2.0351
[1m257/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2592 - loss: 2.0354
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Epoch 28/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2620 - loss: 2.0369 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2615 - loss: 2.0332
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2562 - loss: 2.0309
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Epoch 29/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2680 - loss: 2.0368 
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[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2673 - loss: 2.0217
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Epoch 30/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2632 - loss: 2.0615 
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[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2687 - loss: 2.0283
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2681 - loss: 2.0257
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2680 - loss: 2.0246
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2675 - loss: 2.0235 - val_accuracy: 0.3267 - val_loss: 1.8003
Epoch 31/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2573 - loss: 1.9973 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2596 - loss: 1.9946
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2608 - loss: 1.9958
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2618 - loss: 1.9955
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2632 - loss: 1.9954
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2641 - loss: 1.9963
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2646 - loss: 1.9978
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2651 - loss: 1.9990 - val_accuracy: 0.3295 - val_loss: 1.7923
Epoch 32/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2761 - loss: 1.9638 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2762 - loss: 1.9706
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2766 - loss: 1.9748
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2763 - loss: 1.9793
[1m191/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2762 - loss: 1.9809
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2759 - loss: 1.9820
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2753 - loss: 1.9833
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2748 - loss: 1.9841 - val_accuracy: 0.3293 - val_loss: 1.7925
Epoch 33/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2750 - loss: 1.9765 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2779 - loss: 1.9745
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[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2761 - loss: 1.9798
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Epoch 34/110

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[1m 32/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2787 - loss: 1.9776 
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[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2770 - loss: 1.9860
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Epoch 35/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2946 - loss: 1.9831 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2865 - loss: 1.9828
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[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2797 - loss: 1.9801
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2794 - loss: 1.9784
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2794 - loss: 1.9776 - val_accuracy: 0.3356 - val_loss: 1.7614
Epoch 36/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2500 - loss: 1.9649
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2720 - loss: 1.9856 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2744 - loss: 1.9853
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2792 - loss: 1.9670
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2797 - loss: 1.9658
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2798 - loss: 1.9653 - val_accuracy: 0.3443 - val_loss: 1.7590
Epoch 37/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.3438 - loss: 1.9045
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2916 - loss: 1.9891 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2878 - loss: 1.9824
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2864 - loss: 1.9772
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2861 - loss: 1.9740
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2860 - loss: 1.9711
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2857 - loss: 1.9692
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2857 - loss: 1.9680
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2856 - loss: 1.9673 - val_accuracy: 0.3495 - val_loss: 1.7582
Epoch 38/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.2969 - loss: 1.9838
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2898 - loss: 1.9538 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2938 - loss: 1.9404
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[1m159/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2933 - loss: 1.9353
[1m199/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2926 - loss: 1.9361
[1m240/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2918 - loss: 1.9377
[1m281/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2915 - loss: 1.9393
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Epoch 39/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2768 - loss: 1.9531 
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Epoch 40/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2823 - loss: 1.9466 
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Epoch 41/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2935 - loss: 1.9404 
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[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2939 - loss: 1.9431
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Epoch 42/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3232 - loss: 1.9307 
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[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3104 - loss: 1.9233
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3094 - loss: 1.9220
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3088 - loss: 1.9208
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3084 - loss: 1.9203 - val_accuracy: 0.3537 - val_loss: 1.7219
Epoch 43/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2927 - loss: 1.9684 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2991 - loss: 1.9483
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2976 - loss: 1.9370
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2974 - loss: 1.9360
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Epoch 44/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2921 - loss: 1.9452 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2943 - loss: 1.9284
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Epoch 45/110

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Epoch 46/110

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Epoch 47/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3276 - loss: 1.8911 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3195 - loss: 1.8936
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[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3138 - loss: 1.8880
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3131 - loss: 1.8886 - val_accuracy: 0.3489 - val_loss: 1.6963
Epoch 48/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3074 - loss: 1.8829 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3070 - loss: 1.8872
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3078 - loss: 1.8849
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3084 - loss: 1.8840
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3090 - loss: 1.8829
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3093 - loss: 1.8818
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3096 - loss: 1.8811 - val_accuracy: 0.3608 - val_loss: 1.6977
Epoch 49/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2982 - loss: 1.8988 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3044 - loss: 1.8821
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[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3074 - loss: 1.8775
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3083 - loss: 1.8771
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3091 - loss: 1.8762
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Epoch 50/110

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Epoch 51/110

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Epoch 52/110

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Epoch 53/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3113 - loss: 1.8604 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3184 - loss: 1.8559
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[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3229 - loss: 1.8493
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Epoch 54/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3552 - loss: 1.7672 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3456 - loss: 1.7998
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[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3312 - loss: 1.8289
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Epoch 55/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3200 - loss: 1.8802 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3220 - loss: 1.8641
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Epoch 56/110

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Epoch 57/110

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Epoch 58/110

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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3327 - loss: 1.8221 - val_accuracy: 0.3586 - val_loss: 1.6507
Epoch 59/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3417 - loss: 1.8349 
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[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3343 - loss: 1.8173
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3332 - loss: 1.8174
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Epoch 60/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3304 - loss: 1.8198 
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Epoch 61/110

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Epoch 62/110

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Epoch 63/110

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Epoch 64/110

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Epoch 65/110

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Epoch 66/110

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Epoch 67/110

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Epoch 68/110

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Epoch 69/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3457 - loss: 1.8444 
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[1m254/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3441 - loss: 1.7975
[1m291/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3440 - loss: 1.7960
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3440 - loss: 1.7960 - val_accuracy: 0.3634 - val_loss: 1.6294
Epoch 70/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3441 - loss: 1.7982 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3468 - loss: 1.7885
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3466 - loss: 1.7864
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3461 - loss: 1.7877
[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3456 - loss: 1.7875
[1m220/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3451 - loss: 1.7875
[1m259/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3447 - loss: 1.7878
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Epoch 71/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3325 - loss: 1.8364 
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Epoch 72/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3552 - loss: 1.7432 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3454 - loss: 1.7662
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Epoch 73/110

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[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3315 - loss: 1.8110
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3402 - loss: 1.7944
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Epoch 74/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 25ms/step - accuracy: 0.3125 - loss: 1.8505
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3368 - loss: 1.8003 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3416 - loss: 1.7856
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[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3453 - loss: 1.7736
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3458 - loss: 1.7721
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3461 - loss: 1.7712
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3462 - loss: 1.7709 - val_accuracy: 0.3704 - val_loss: 1.6106
Epoch 75/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2500 - loss: 2.0370
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3397 - loss: 1.8131 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3392 - loss: 1.8038
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3407 - loss: 1.7944
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3409 - loss: 1.7896
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3411 - loss: 1.7859
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3411 - loss: 1.7834
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3409 - loss: 1.7814
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3408 - loss: 1.7806 - val_accuracy: 0.3726 - val_loss: 1.6233
Epoch 76/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3791 - loss: 1.7278 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3643 - loss: 1.7422
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[1m145/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3522 - loss: 1.7544
[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3496 - loss: 1.7574
[1m217/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3482 - loss: 1.7596
[1m257/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3471 - loss: 1.7617
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Epoch 77/110

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Epoch 78/110

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[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3483 - loss: 1.7707
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Epoch 79/110

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[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3586 - loss: 1.7250
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[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3520 - loss: 1.7400
[1m275/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3514 - loss: 1.7417
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3511 - loss: 1.7427 - val_accuracy: 0.3789 - val_loss: 1.6069
Epoch 80/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3273 - loss: 1.8106 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3349 - loss: 1.7913
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3380 - loss: 1.7836
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3401 - loss: 1.7775
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3414 - loss: 1.7739
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3420 - loss: 1.7717
[1m275/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3424 - loss: 1.7699
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3425 - loss: 1.7692 - val_accuracy: 0.3702 - val_loss: 1.6260
Epoch 81/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3457 - loss: 1.7709 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3464 - loss: 1.7624
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[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3476 - loss: 1.7609
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3476 - loss: 1.7611
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3476 - loss: 1.7614
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3479 - loss: 1.7613
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Epoch 82/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3460 - loss: 1.7230 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3476 - loss: 1.7265
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3491 - loss: 1.7283
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3490 - loss: 1.7310
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3490 - loss: 1.7327
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3491 - loss: 1.7334
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3491 - loss: 1.7340 - val_accuracy: 0.3704 - val_loss: 1.6108
Epoch 83/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3281 - loss: 1.6085
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3414 - loss: 1.7198 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3457 - loss: 1.7332
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3477 - loss: 1.7381
[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3486 - loss: 1.7423
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3490 - loss: 1.7450
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3493 - loss: 1.7469
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3496 - loss: 1.7480
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3497 - loss: 1.7483 - val_accuracy: 0.3756 - val_loss: 1.6148
Epoch 84/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.2812 - loss: 2.0145
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3272 - loss: 1.7619 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3296 - loss: 1.7638
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3303 - loss: 1.7655
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3321 - loss: 1.7632
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3337 - loss: 1.7624
[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3351 - loss: 1.7610
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3361 - loss: 1.7596
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3368 - loss: 1.7584 - val_accuracy: 0.3661 - val_loss: 1.6106

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:12[0m 852ms/step
[1m63/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 810us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 9ms/step
Saved model to disk.

Accuracy capturado en la ejecución 28: 35.77 [%]
F1-score capturado en la ejecución 28: 35.38 [%]

=== EJECUCIÓN 29 ===

--- TRAIN (ejecución 29) ---

--- TEST (ejecución 29) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18676, 6, 250)

[1m  1/584[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:47[0m 905ms/step
[1m 61/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 836us/step  
[1m128/584[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 791us/step
[1m199/584[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 760us/step
[1m271/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 744us/step
[1m344/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 732us/step
[1m415/584[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 727us/step
[1m489/584[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 720us/step
[1m561/584[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 716us/step
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m66/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 774us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 72/169[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 705us/step
[1m148/169[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 684us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step
Global accuracy score (validation) = 36.61 [%]
Global F1 score (validation) = 36.18 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[2.00213030e-01 1.72775745e-01 2.04193622e-01 ... 5.75520698e-06
  1.80922344e-01 3.82259414e-02]
 [2.10506141e-01 1.90150872e-01 2.10163534e-01 ... 1.04677320e-05
  1.56487480e-01 1.86100863e-02]
 [1.91077605e-01 2.05830574e-01 2.04061642e-01 ... 2.85646456e-05
  1.73055351e-01 3.83823663e-02]
 ...
 [2.09510103e-01 2.07782671e-01 2.05126360e-01 ... 2.43509203e-05
  1.53910518e-01 1.43800480e-02]
 [1.89477101e-01 1.91096961e-01 2.01871678e-01 ... 6.19582788e-05
  1.88163653e-01 1.81082115e-02]
 [8.00626501e-02 1.06407955e-01 9.84200314e-02 ... 1.58996426e-03
  1.43519521e-01 8.07191059e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 43.43 [%]
Global accuracy score (test) = 35.15 [%]
Global F1 score (train) = 42.09 [%]
Global F1 score (test) = 33.3 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.27      0.32      0.30       184
 CAMINAR CON MÓVIL O LIBRO       0.24      0.50      0.33       184
       CAMINAR USUAL SPEED       0.13      0.05      0.08       184
            CAMINAR ZIGZAG       0.10      0.05      0.06       184
          DE PIE BARRIENDO       0.21      0.30      0.24       184
   DE PIE DOBLANDO TOALLAS       0.28      0.09      0.14       184
    DE PIE MOVIENDO LIBROS       0.27      0.35      0.30       184
          DE PIE USANDO PC       0.39      0.61      0.48       184
        FASE REPOSO CON K5       0.49      0.85      0.62       184
INCREMENTAL CICLOERGOMETRO       0.88      0.61      0.72       184
           SENTADO LEYENDO       0.37      0.33      0.35       184
         SENTADO USANDO PC       0.20      0.08      0.12       184
      SENTADO VIENDO LA TV       0.34      0.35      0.34       184
   SUBIR Y BAJAR ESCALERAS       0.16      0.09      0.12       184
                    TROTAR       0.90      0.71      0.80       161

                  accuracy                           0.35      2737
                 macro avg       0.35      0.35      0.33      2737
              weighted avg       0.34      0.35      0.33      2737

2025-10-28 14:34:17.861678: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 14:34:17.872811: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1761658457.885636 2452865 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761658457.889734 2452865 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761658457.900074 2452865 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761658457.900092 2452865 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761658457.900095 2452865 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761658457.900098 2452865 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 14:34:17.903088: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1761658460.262120 2452865 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13733 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/110
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761658461.964266 2452994 service.cc:152] XLA service 0x7e5fc000bda0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761658461.964328 2452994 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 14:34:21.997378: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
I0000 00:00:1761658462.162032 2452994 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761658464.442962 2452994 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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Epoch 2/110

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Epoch 3/110

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Epoch 4/110

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Epoch 5/110

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Epoch 6/110

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Epoch 7/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1591 - loss: 2.6826 
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1473 - loss: 2.6648
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1480 - loss: 2.6600 - val_accuracy: 0.2102 - val_loss: 2.2676
Epoch 8/110

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[1m 42/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1393 - loss: 2.6350 
[1m 84/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1415 - loss: 2.6284
[1m125/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1438 - loss: 2.6183
[1m165/292[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1465 - loss: 2.6104
[1m203/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1482 - loss: 2.6056
[1m243/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1496 - loss: 2.6013
[1m282/292[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1507 - loss: 2.5978
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1509 - loss: 2.5970 - val_accuracy: 0.2250 - val_loss: 2.2308
Epoch 9/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1485 - loss: 2.5448 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1547 - loss: 2.5417
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1599 - loss: 2.5321
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Epoch 10/110

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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1715 - loss: 2.4773
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Epoch 11/110

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[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1812 - loss: 2.4198
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1802 - loss: 2.4162
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Epoch 12/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1688 - loss: 2.4163 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1726 - loss: 2.4139
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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1819 - loss: 2.3956
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1823 - loss: 2.3940
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1825 - loss: 2.3928 - val_accuracy: 0.2590 - val_loss: 2.0898
Epoch 13/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2812 - loss: 2.2367
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2095 - loss: 2.3386 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2021 - loss: 2.3515
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2001 - loss: 2.3517
[1m151/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1989 - loss: 2.3497
[1m189/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1985 - loss: 2.3475
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1981 - loss: 2.3463
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1978 - loss: 2.3448
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1974 - loss: 2.3435 - val_accuracy: 0.2514 - val_loss: 2.0452
Epoch 14/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.1406 - loss: 2.3959
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2024 - loss: 2.3274 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2017 - loss: 2.3283
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1991 - loss: 2.3293
[1m155/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1982 - loss: 2.3266
[1m195/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1978 - loss: 2.3239
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1975 - loss: 2.3215
[1m276/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1975 - loss: 2.3188
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1975 - loss: 2.3178 - val_accuracy: 0.2612 - val_loss: 2.0227
Epoch 15/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.1875 - loss: 2.3586
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1908 - loss: 2.3340 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1942 - loss: 2.3213
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[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1975 - loss: 2.3043
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.1991 - loss: 2.2910 - val_accuracy: 0.2735 - val_loss: 1.9995
Epoch 16/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2188 - loss: 2.1413
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2257 - loss: 2.1963 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2209 - loss: 2.2134
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[1m147/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2166 - loss: 2.2301
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[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2140 - loss: 2.2366
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Epoch 17/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2188 - loss: 2.1283
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2285 - loss: 2.2021 
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2197 - loss: 2.2137
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[1m229/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2185 - loss: 2.2159
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2180 - loss: 2.2163 - val_accuracy: 0.2834 - val_loss: 1.9469
Epoch 18/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2344 - loss: 2.1332
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2121 - loss: 2.2037 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2101 - loss: 2.2098
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2097 - loss: 2.2111
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2100 - loss: 2.2097
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2106 - loss: 2.2077
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2113 - loss: 2.2057
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2117 - loss: 2.2040
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2118 - loss: 2.2033 - val_accuracy: 0.2857 - val_loss: 1.9353
Epoch 19/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2656 - loss: 2.1211
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2157 - loss: 2.1843 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2165 - loss: 2.1896
[1m118/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2191 - loss: 2.1878
[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2202 - loss: 2.1858
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2208 - loss: 2.1841
[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2210 - loss: 2.1825
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2210 - loss: 2.1810
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2211 - loss: 2.1804 - val_accuracy: 0.2821 - val_loss: 1.9203
Epoch 20/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2188 - loss: 2.2430
[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2115 - loss: 2.2000 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2141 - loss: 2.1931
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[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2198 - loss: 2.1778
[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2210 - loss: 2.1740
[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2221 - loss: 2.1709
[1m269/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2228 - loss: 2.1688
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2233 - loss: 2.1677 - val_accuracy: 0.3080 - val_loss: 1.8891
Epoch 21/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2331 - loss: 2.1292 
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2328 - loss: 2.1330
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Epoch 22/110

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[1m 34/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2243 - loss: 2.1139 
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[1m219/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2297 - loss: 2.1220
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Epoch 23/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2521 - loss: 2.1197 
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2433 - loss: 2.1135
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2428 - loss: 2.1119
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2425 - loss: 2.1110 - val_accuracy: 0.3064 - val_loss: 1.8509
Epoch 24/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2344 - loss: 2.2371
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2508 - loss: 2.1009 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2494 - loss: 2.0992
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[1m149/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2456 - loss: 2.1006
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2447 - loss: 2.0998
[1m226/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2443 - loss: 2.0987
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2440 - loss: 2.0979
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2438 - loss: 2.0976 - val_accuracy: 0.3177 - val_loss: 1.8357
Epoch 25/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1875 - loss: 2.2036
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2381 - loss: 2.1089 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2370 - loss: 2.1030
[1m111/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2368 - loss: 2.1012
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2371 - loss: 2.0983
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2375 - loss: 2.0962
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2380 - loss: 2.0944
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2384 - loss: 2.0923
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2388 - loss: 2.0911 - val_accuracy: 0.3332 - val_loss: 1.8336
Epoch 26/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2031 - loss: 2.0251
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2441 - loss: 2.0468 
[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2492 - loss: 2.0573
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[1m194/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2515 - loss: 2.0652
[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2517 - loss: 2.0656
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2517 - loss: 2.0659
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2518 - loss: 2.0661 - val_accuracy: 0.3230 - val_loss: 1.8231
Epoch 27/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2344 - loss: 2.0465
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2541 - loss: 2.0603 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2556 - loss: 2.0468
[1m116/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2551 - loss: 2.0434
[1m152/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2552 - loss: 2.0408
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2548 - loss: 2.0405
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2547 - loss: 2.0405
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2549 - loss: 2.0408
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2550 - loss: 2.0411 - val_accuracy: 0.3295 - val_loss: 1.8162
Epoch 28/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2188 - loss: 2.0451
[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2341 - loss: 2.0736 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2379 - loss: 2.0699
[1m117/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2396 - loss: 2.0683
[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2418 - loss: 2.0639
[1m199/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2436 - loss: 2.0596
[1m239/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2446 - loss: 2.0573
[1m276/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2452 - loss: 2.0561
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2455 - loss: 2.0555 - val_accuracy: 0.3293 - val_loss: 1.8040
Epoch 29/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.2969 - loss: 2.0567
[1m 32/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2730 - loss: 2.0437 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2693 - loss: 2.0424
[1m109/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2689 - loss: 2.0392
[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2687 - loss: 2.0371
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2681 - loss: 2.0349
[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2672 - loss: 2.0332
[1m266/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2665 - loss: 2.0321
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2661 - loss: 2.0315 - val_accuracy: 0.3234 - val_loss: 1.8043
Epoch 30/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.2188 - loss: 2.0021
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2752 - loss: 1.9895 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2720 - loss: 2.0033
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2692 - loss: 2.0097
[1m156/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2673 - loss: 2.0136
[1m197/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2661 - loss: 2.0152
[1m235/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2654 - loss: 2.0159
[1m272/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2649 - loss: 2.0164
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2647 - loss: 2.0166 - val_accuracy: 0.3169 - val_loss: 1.7953
Epoch 31/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.2188 - loss: 2.1539
[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2575 - loss: 2.0387 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2633 - loss: 2.0263
[1m115/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2661 - loss: 2.0208
[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2665 - loss: 2.0181
[1m193/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2668 - loss: 2.0164
[1m230/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2673 - loss: 2.0150
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2677 - loss: 2.0138
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2680 - loss: 2.0132 - val_accuracy: 0.3365 - val_loss: 1.7811
Epoch 32/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2563 - loss: 2.0028 
[1m 70/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2585 - loss: 2.0010
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Epoch 33/110

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Epoch 34/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2885 - loss: 1.9983 
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2808 - loss: 1.9896
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2808 - loss: 1.9885
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2808 - loss: 1.9879 - val_accuracy: 0.3419 - val_loss: 1.7622
Epoch 35/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2912 - loss: 1.9602 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2861 - loss: 1.9672
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[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2830 - loss: 1.9704
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2823 - loss: 1.9719
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2814 - loss: 1.9728
[1m268/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2810 - loss: 1.9730
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2809 - loss: 1.9730 - val_accuracy: 0.3493 - val_loss: 1.7450
Epoch 36/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3750 - loss: 1.7537
[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3057 - loss: 1.9291 
[1m 68/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2987 - loss: 1.9381
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[1m141/292[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2933 - loss: 1.9469
[1m180/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2921 - loss: 1.9493
[1m216/292[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2911 - loss: 1.9513
[1m257/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2905 - loss: 1.9525
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Epoch 37/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2944 - loss: 1.9330 
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Epoch 38/110

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Epoch 39/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3041 - loss: 1.9227 
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Epoch 40/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3071 - loss: 1.9442 
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[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2997 - loss: 1.9261
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2994 - loss: 1.9262
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.2992 - loss: 1.9260 - val_accuracy: 0.3484 - val_loss: 1.7127
Epoch 41/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2999 - loss: 1.8949 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2973 - loss: 1.9060
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[1m233/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2981 - loss: 1.9095
[1m273/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2982 - loss: 1.9100
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Epoch 42/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2894 - loss: 1.9367 
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Epoch 43/110

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Epoch 44/110

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Epoch 45/110

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[1m 39/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3145 - loss: 1.8729 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3133 - loss: 1.8753
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[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3068 - loss: 1.8879
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3064 - loss: 1.8895 - val_accuracy: 0.3510 - val_loss: 1.6765
Epoch 46/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3750 - loss: 1.7027
[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3198 - loss: 1.8452 
[1m 72/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3138 - loss: 1.8587
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[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3085 - loss: 1.8763
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3078 - loss: 1.8784
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3073 - loss: 1.8805 - val_accuracy: 0.3591 - val_loss: 1.6649
Epoch 47/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3044 - loss: 1.8706 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3062 - loss: 1.8729
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Epoch 48/110

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Epoch 49/110

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Epoch 50/110

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Epoch 51/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3013 - loss: 1.8588 
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[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3215 - loss: 1.8481
[1m267/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3222 - loss: 1.8478
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3224 - loss: 1.8478 - val_accuracy: 0.3513 - val_loss: 1.6431
Epoch 52/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3307 - loss: 1.8493 
[1m 80/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3258 - loss: 1.8448
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3226 - loss: 1.8455
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3223 - loss: 1.8459
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Epoch 53/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3501 - loss: 1.8256 
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Epoch 54/110

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Epoch 55/110

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Epoch 56/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3273 - loss: 1.8105 
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[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3238 - loss: 1.8378
[1m265/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3240 - loss: 1.8369
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3241 - loss: 1.8363 - val_accuracy: 0.3687 - val_loss: 1.6272
Epoch 57/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3266 - loss: 1.8396 
[1m 79/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3239 - loss: 1.8437
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[1m154/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3235 - loss: 1.8374
[1m192/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3235 - loss: 1.8364
[1m231/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3234 - loss: 1.8361
[1m270/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3232 - loss: 1.8357
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3233 - loss: 1.8352 - val_accuracy: 0.3600 - val_loss: 1.6294
Epoch 58/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3082 - loss: 1.8774 
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[1m228/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3191 - loss: 1.8332
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Epoch 59/110

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Epoch 60/110

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Epoch 61/110

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Epoch 62/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3296 - loss: 1.8203 
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Epoch 63/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3435 - loss: 1.8083 
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Epoch 64/110

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Epoch 65/110

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Epoch 66/110

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Epoch 67/110

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[1m186/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3400 - loss: 1.7660
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3392 - loss: 1.7710 - val_accuracy: 0.3713 - val_loss: 1.6055
Epoch 68/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3469 - loss: 1.8023 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3441 - loss: 1.7907
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[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3390 - loss: 1.7844
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Epoch 69/110

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Epoch 70/110

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Epoch 71/110

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Epoch 72/110

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[1m 37/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3356 - loss: 1.7880 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3370 - loss: 1.7829
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[1m232/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3380 - loss: 1.7722
[1m271/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3380 - loss: 1.7714
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3379 - loss: 1.7711 - val_accuracy: 0.3717 - val_loss: 1.5978
Epoch 73/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3461 - loss: 1.7650 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3400 - loss: 1.7699
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[1m223/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3374 - loss: 1.7754
[1m260/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3376 - loss: 1.7751
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3379 - loss: 1.7746 - val_accuracy: 0.3621 - val_loss: 1.6018
Epoch 74/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3561 - loss: 1.7561 
[1m 82/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3532 - loss: 1.7513
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[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3495 - loss: 1.7589
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Epoch 75/110

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Epoch 76/110

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Epoch 77/110

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[1m 76/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3477 - loss: 1.7342
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3448 - loss: 1.7450 - val_accuracy: 0.3621 - val_loss: 1.5946
Epoch 78/110

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[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3568 - loss: 1.7145 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3593 - loss: 1.7196
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[1m196/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3555 - loss: 1.7342
[1m236/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3549 - loss: 1.7366
[1m276/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3545 - loss: 1.7381
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3543 - loss: 1.7385 - val_accuracy: 0.3652 - val_loss: 1.6012
Epoch 79/110

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[1m 40/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3360 - loss: 1.7727 
[1m 77/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3428 - loss: 1.7540
[1m114/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3448 - loss: 1.7508
[1m150/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3453 - loss: 1.7490
[1m188/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3455 - loss: 1.7484
[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3459 - loss: 1.7471
[1m263/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3462 - loss: 1.7464
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Epoch 80/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3352 - loss: 1.7706 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3443 - loss: 1.7534
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Epoch 81/110

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Epoch 82/110

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[1m234/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3604 - loss: 1.7501
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Epoch 83/110

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[1m 41/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3491 - loss: 1.7666 
[1m 78/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3467 - loss: 1.7603
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[1m238/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3467 - loss: 1.7428
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[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3475 - loss: 1.7401 - val_accuracy: 0.3739 - val_loss: 1.5897
Epoch 84/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3565 - loss: 1.7384 
[1m 71/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3562 - loss: 1.7309
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[1m184/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3545 - loss: 1.7263
[1m222/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3539 - loss: 1.7266
[1m261/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3537 - loss: 1.7268
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3536 - loss: 1.7267 - val_accuracy: 0.3767 - val_loss: 1.5870
Epoch 85/110

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[1m 38/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3681 - loss: 1.7198 
[1m 75/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3638 - loss: 1.7112
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[1m225/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3554 - loss: 1.7257
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Epoch 86/110

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[1m 36/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3507 - loss: 1.6810 
[1m 74/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3516 - loss: 1.6918
[1m111/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3510 - loss: 1.6996
[1m146/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3497 - loss: 1.7053
[1m185/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3489 - loss: 1.7111
[1m222/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3488 - loss: 1.7138
[1m264/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3487 - loss: 1.7161
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3488 - loss: 1.7174 - val_accuracy: 0.3784 - val_loss: 1.5966
Epoch 87/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.3438 - loss: 1.9210
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[1m 81/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3404 - loss: 1.7009
[1m119/292[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3432 - loss: 1.7036
[1m158/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3445 - loss: 1.7065
[1m199/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3453 - loss: 1.7093
[1m239/292[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3459 - loss: 1.7113
[1m277/292[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3466 - loss: 1.7124
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3468 - loss: 1.7127 - val_accuracy: 0.3795 - val_loss: 1.5993
Epoch 88/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3750 - loss: 1.8145
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[1m 70/292[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3574 - loss: 1.7184
[1m110/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3546 - loss: 1.7221
[1m148/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3534 - loss: 1.7246
[1m187/292[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3529 - loss: 1.7257
[1m224/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3524 - loss: 1.7268
[1m262/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3520 - loss: 1.7273
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3519 - loss: 1.7275 - val_accuracy: 0.3796 - val_loss: 1.5914
Epoch 89/110

[1m  1/292[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 28ms/step - accuracy: 0.4219 - loss: 1.6220
[1m 35/292[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3625 - loss: 1.6907 
[1m 73/292[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3577 - loss: 1.7044
[1m112/292[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3555 - loss: 1.7124
[1m153/292[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3546 - loss: 1.7154
[1m190/292[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3543 - loss: 1.7178
[1m227/292[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3542 - loss: 1.7196
[1m262/292[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3541 - loss: 1.7208
[1m292/292[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3541 - loss: 1.7212 - val_accuracy: 0.3721 - val_loss: 1.5929

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:10[0m 833ms/step
[1m60/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 850us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 9ms/step
Saved model to disk.

Accuracy capturado en la ejecución 29: 35.15 [%]
F1-score capturado en la ejecución 29: 33.3 [%]

=== EJECUCIÓN 30 ===

--- TRAIN (ejecución 30) ---

--- TEST (ejecución 30) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 64)          │        80,064 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 64)          │        20,544 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 64)          │           128 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 64)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 64)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │           975 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 101,839 (397.81 KB)
 Trainable params: 101,839 (397.81 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18676, 6, 250)

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[1m 68/584[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 752us/step  
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[1m216/584[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 703us/step
[1m275/584[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 737us/step
[1m345/584[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 732us/step
[1m417/584[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 727us/step
[1m482/584[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 734us/step
[1m547/584[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 739us/step
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m584/584[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m68/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 749us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 8ms/step

[1m  1/169[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 61/169[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 842us/step
[1m133/169[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 763us/step
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m169/169[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 37.21 [%]
Global F1 score (validation) = 36.07 [%]
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[[2.2023782e-01 1.8397874e-01 1.9427525e-01 ... 1.1549113e-06
  1.6657901e-01 1.9513719e-02]
 [2.2678179e-01 1.9957322e-01 2.1158464e-01 ... 3.4546779e-06
  1.2974401e-01 7.0691728e-03]
 [2.0886649e-01 2.0353301e-01 2.0222916e-01 ... 7.4137456e-06
  1.6594820e-01 1.0161059e-02]
 ...
 [2.0288508e-01 2.0272289e-01 2.0349158e-01 ... 4.4824430e-04
  1.4848904e-01 1.2538373e-02]
 [2.0896278e-01 2.1020430e-01 2.0796339e-01 ... 7.3768701e-05
  1.3471030e-01 7.3077665e-03]
 [7.1525022e-02 9.4275929e-02 7.6677240e-02 ... 1.1219772e-03
  7.3098563e-02 2.1326481e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 43.85 [%]
Global accuracy score (test) = 37.41 [%]
Global F1 score (train) = 42.17 [%]
Global F1 score (test) = 35.83 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.25      0.53      0.34       184
 CAMINAR CON MÓVIL O LIBRO       0.16      0.17      0.17       184
       CAMINAR USUAL SPEED       0.50      0.02      0.03       184
            CAMINAR ZIGZAG       0.23      0.26      0.24       184
          DE PIE BARRIENDO       0.26      0.52      0.35       184
   DE PIE DOBLANDO TOALLAS       0.24      0.12      0.16       184
    DE PIE MOVIENDO LIBROS       0.30      0.28      0.29       184
          DE PIE USANDO PC       0.45      0.59      0.51       184
        FASE REPOSO CON K5       0.58      0.85      0.69       184
INCREMENTAL CICLOERGOMETRO       0.89      0.58      0.70       184
           SENTADO LEYENDO       0.32      0.45      0.37       184
         SENTADO USANDO PC       0.37      0.12      0.18       184
      SENTADO VIENDO LA TV       0.32      0.26      0.29       184
   SUBIR Y BAJAR ESCALERAS       0.40      0.17      0.24       184
                    TROTAR       0.89      0.73      0.81       161

                  accuracy                           0.37      2737
                 macro avg       0.41      0.38      0.36      2737
              weighted avg       0.41      0.37      0.35      2737


Accuracy capturado en la ejecución 30: 37.41 [%]
F1-score capturado en la ejecución 30: 35.83 [%]

=== RESUMEN FINAL ===
Accuracies: [37.6, 37.38, 37.01, 36.32, 35.99, 36.32, 36.32, 35.15, 36.65, 36.54, 36.06, 37.82, 34.78, 34.2, 35.51, 36.21, 35.0, 34.53, 36.39, 36.5, 35.07, 36.76, 34.82, 36.54, 35.51, 35.51, 36.24, 35.77, 35.15, 37.41]
F1-scores: [36.87, 35.01, 36.23, 36.2, 34.67, 34.9, 35.01, 33.73, 35.92, 36.14, 34.35, 37.35, 33.52, 33.63, 33.59, 34.82, 34.26, 34.11, 35.93, 36.17, 34.81, 36.36, 34.41, 35.83, 33.73, 33.05, 35.07, 35.38, 33.3, 35.83]
Accuracy mean: 36.0353 | std: 0.9221
F1 mean: 35.0060 | std: 1.1255

Resultados guardados en /mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_C/case_C_ESANN_acc_gyr_17_classes/metrics_test.npz
