2025-11-05 10:34:28.656200: 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`.
2025-11-05 10:34:28.668069: 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 00:00:1762335268.682001 2935316 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762335268.686358 2935316 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762335268.696801 2935316 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335268.696821 2935316 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335268.696823 2935316 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335268.696824 2935316 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:34:28.700080: 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(
2025-11-05 10:34:32,636	INFO worker.py:1927 -- Started a local Ray instance.
2025-11-05 10:34:33,326	INFO tune.py:253 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `Tuner(...)`.
2025-11-05 10:34:33,400	INFO trial.py:182 -- Creating a new dirname dir_a2ecf_c69e because trial dirname 'dir_a2ecf' already exists.
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2025-11-05 10:34:33,427	INFO trial.py:182 -- Creating a new dirname dir_a2ecf_3a42 because trial dirname 'dir_a2ecf' already exists.
2025-11-05 10:34:33,430	INFO trial.py:182 -- Creating a new dirname dir_a2ecf_d09f because trial dirname 'dir_a2ecf' already exists.
2025-11-05 10:34:33,433	INFO trial.py:182 -- Creating a new dirname dir_a2ecf_b9b2 because trial dirname 'dir_a2ecf' already exists.
2025-11-05 10:34:33,436	INFO trial.py:182 -- Creating a new dirname dir_a2ecf_f5b7 because trial dirname 'dir_a2ecf' already exists.
2025-11-05 10:34:33,439	INFO trial.py:182 -- Creating a new dirname dir_a2ecf_b3f0 because trial dirname 'dir_a2ecf' already exists.
2025-11-05 10:34:33,443	INFO trial.py:182 -- Creating a new dirname dir_a2ecf_52e0 because trial dirname 'dir_a2ecf' already exists.
2025-11-05 10:34:33,453	INFO trial.py:182 -- Creating a new dirname dir_a2ecf_4ef8 because trial dirname 'dir_a2ecf' already exists.
2025-11-05 10:34:33,459	INFO trial.py:182 -- Creating a new dirname dir_a2ecf_0bc0 because trial dirname 'dir_a2ecf' already exists.
1 GPU(s) detected and VRAM set to crossover mode..
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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_M/case_M_ESANN_acc_superclasses_CPA_METs/ESANN_hyperparameters_tuning
To visualize your results with TensorBoard, run: `tensorboard --logdir /tmp/ray/session_2025-11-05_10-34-30_923930_2935316/artifacts/2025-11-05_10-34-33/ESANN_hyperparameters_tuning/driver_artifacts`

Trial status: 20 PENDING
Current time: 2025-11-05 10:34:33. Total running time: 0s
Logical resource usage: 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_a2ecf    PENDING            2   adam            relu                                   32                128                  5          0.00125802          53 │
│ trial_a2ecf    PENDING            4   rmsprop         tanh                                   32                 64                  5          0.000458509         75 │
│ trial_a2ecf    PENDING            3   adam            tanh                                   64                 64                  3          2.2792e-05          85 │
│ trial_a2ecf    PENDING            4   adam            relu                                  128                 32                  3          0.000635588        123 │
│ trial_a2ecf    PENDING            3   rmsprop         tanh                                   64                 32                  5          0.000121978         82 │
│ trial_a2ecf    PENDING            4   adam            relu                                  128                128                  3          0.000315664         95 │
│ trial_a2ecf    PENDING            2   adam            relu                                   64                128                  3          6.73958e-05        109 │
│ trial_a2ecf    PENDING            3   adam            relu                                   32                128                  5          3.53159e-05        143 │
│ trial_a2ecf    PENDING            2   rmsprop         tanh                                  128                128                  3          0.00302364         124 │
│ trial_a2ecf    PENDING            4   adam            relu                                   32                 32                  5          0.00089052         105 │
│ trial_a2ecf    PENDING            3   adam            tanh                                   32                 32                  3          1.24551e-05        117 │
│ trial_a2ecf    PENDING            4   adam            tanh                                  128                 32                  3          0.000781525         69 │
│ trial_a2ecf    PENDING            4   adam            tanh                                  128                128                  3          0.000165006         85 │
│ trial_a2ecf    PENDING            3   rmsprop         relu                                   64                 32                  5          1.53927e-05        111 │
│ trial_a2ecf    PENDING            4   adam            tanh                                   32                 32                  5          0.00375735         100 │
│ trial_a2ecf    PENDING            4   adam            relu                                   64                 64                  5          0.00494679         109 │
│ trial_a2ecf    PENDING            4   adam            relu                                  128                 32                  3          2.97458e-05        109 │
│ trial_a2ecf    PENDING            3   adam            tanh                                   64                128                  5          0.000417164         75 │
│ trial_a2ecf    PENDING            4   rmsprop         relu                                   32                128                  3          0.00366154         135 │
│ trial_a2ecf    PENDING            3   rmsprop         relu                                   32                 64                  5          0.00102226          92 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_a2ecf started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_a2ecf config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                            69 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00078 │
╰──────────────────────────────────────╯
Trial trial_a2ecf started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_a2ecf config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            75 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00042 │
╰──────────────────────────────────────╯
Trial trial_a2ecf started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_a2ecf config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                           117 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_a2ecf started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_a2ecf config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                           109 │
│ funcion_activacion              relu │
│ numero_filtros                   128 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00007 │
╰──────────────────────────────────────╯
Trial trial_a2ecf started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_a2ecf config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                           100 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00376 │
╰──────────────────────────────────────╯
Trial trial_a2ecf started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_a2ecf config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                           143 │
│ funcion_activacion              relu │
│ numero_filtros                   128 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00004 │
╰──────────────────────────────────────╯
Trial trial_a2ecf started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_a2ecf config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                            95 │
│ funcion_activacion              relu │
│ numero_filtros                   128 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00032 │
╰──────────────────────────────────────╯
Trial trial_a2ecf started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_a2ecf config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            92 │
│ funcion_activacion              relu │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00102 │
╰──────────────────────────────────────╯
Trial trial_a2ecf started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_a2ecf config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                            75 │
│ funcion_activacion              tanh │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00046 │
╰──────────────────────────────────────╯
Trial trial_a2ecf started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_a2ecf config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                           123 │
│ funcion_activacion              relu │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00064 │
╰──────────────────────────────────────╯
Trial trial_a2ecf started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_a2ecf config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            82 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00012 │
╰──────────────────────────────────────╯
Trial trial_a2ecf started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_a2ecf config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                            85 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00017 │
╰──────────────────────────────────────╯
Trial trial_a2ecf started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_a2ecf config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                           111 │
│ funcion_activacion              relu │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00002 │
╰──────────────────────────────────────╯
Trial trial_a2ecf started with configuration:
[36m(train_cnn_ray_tune pid=2936977)[0m 2025-11-05 10:34:36.708016: 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=2936977)[0m 2025-11-05 10:34:36.730412: 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=2936972)[0m WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
[36m(train_cnn_ray_tune pid=2936972)[0m E0000 00:00:1762335276.726627 2938098 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=2936972)[0m E0000 00:00:1762335276.735533 2938098 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=2936977)[0m W0000 00:00:1762335276.788838 2938096 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=2936977)[0m W0000 00:00:1762335276.788896 2938096 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=2936977)[0m W0000 00:00:1762335276.788902 2938096 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=2936977)[0m W0000 00:00:1762335276.788906 2938096 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=2936977)[0m 2025-11-05 10:34:36.794911: 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=2936977)[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=2936972)[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=2936972)[0m   warnings.warn(
[36m(train_cnn_ray_tune pid=2936977)[0m 2025-11-05 10:34:39.993593: 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=2936977)[0m 2025-11-05 10:34:39.993644: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:167] env: CUDA_VISIBLE_DEVICES=""
[36m(train_cnn_ray_tune pid=2936977)[0m 2025-11-05 10:34:39.993653: 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=2936977)[0m 2025-11-05 10:34:39.993658: 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=2936977)[0m 2025-11-05 10:34:39.993665: 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=2936977)[0m 2025-11-05 10:34:39.993669: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:190] hostname: simur-MS-7B94
[36m(train_cnn_ray_tune pid=2936977)[0m 2025-11-05 10:34:39.993929: 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=2936977)[0m 2025-11-05 10:34:39.993970: 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=2936977)[0m 2025-11-05 10:34:39.993977: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:291] kernel version seems to match DSO: 570.133.7
╭──────────────────────────────────────╮
│ Trial trial_a2ecf config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            53 │
│ funcion_activacion              relu │
│ numero_filtros                   128 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00126 │
╰──────────────────────────────────────╯
Trial trial_a2ecf started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_a2ecf config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                           109 │
│ funcion_activacion              relu │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00495 │
╰──────────────────────────────────────╯
Trial trial_a2ecf started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_a2ecf config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                           124 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00302 │
╰──────────────────────────────────────╯
Trial trial_a2ecf started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_a2ecf config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            85 │
│ funcion_activacion              tanh │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00002 │
╰──────────────────────────────────────╯
Trial trial_a2ecf started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_a2ecf config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                           105 │
│ funcion_activacion              relu │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00089 │
╰──────────────────────────────────────╯
Trial trial_a2ecf started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_a2ecf config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                           135 │
│ funcion_activacion              relu │
│ numero_filtros                   128 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00366 │
╰──────────────────────────────────────╯
Trial trial_a2ecf started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_a2ecf config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                           109 │
│ funcion_activacion              relu │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00003 │
╰──────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2936973)[0m Model: "sequential"
[36m(train_cnn_ray_tune pid=2936977)[0m ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
[36m(train_cnn_ray_tune pid=2936977)[0m ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
[36m(train_cnn_ray_tune pid=2936977)[0m ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
[36m(train_cnn_ray_tune pid=2936977)[0m │ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
[36m(train_cnn_ray_tune pid=2936977)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=2936977)[0m │ layer_normalization             │ (None, 3, 32)          │            64 │
[36m(train_cnn_ray_tune pid=2936977)[0m │ (LayerNormalization)            │                        │               │
[36m(train_cnn_ray_tune pid=2936977)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=2936977)[0m │ dropout (Dropout)               │ (None, 3, 32)          │             0 │
[36m(train_cnn_ray_tune pid=2936977)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=2936977)[0m │ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
[36m(train_cnn_ray_tune pid=2936977)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=2936977)[0m │ layer_normalization_1           │ (None, 3, 32)          │            64 │
[36m(train_cnn_ray_tune pid=2936977)[0m │ (LayerNormalization)            │                        │               │
[36m(train_cnn_ray_tune pid=2936977)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=2936977)[0m │ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
[36m(train_cnn_ray_tune pid=2936977)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=2936977)[0m │ conv1d_2 (Conv1D)               │ (None, 3, 32)          │         3,104 │
[36m(train_cnn_ray_tune pid=2936977)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=2936977)[0m │ layer_normalization_2           │ (None, 3, 32)          │            64 │
[36m(train_cnn_ray_tune pid=2936977)[0m │ (LayerNormalization)            │                        │               │
[36m(train_cnn_ray_tune pid=2936977)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=2936977)[0m │ dropout_2 (Dropout)             │ (None, 3, 32)          │             0 │
[36m(train_cnn_ray_tune pid=2936977)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=2936977)[0m │ conv1d_3 (Conv1D)               │ (None, 3, 32)          │         3,104 │
[36m(train_cnn_ray_tune pid=2936977)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=2936977)[0m │ layer_normalization_3           │ (None, 3, 32)          │            64 │
[36m(train_cnn_ray_tune pid=2936977)[0m │ (LayerNormalization)            │                        │               │
[36m(train_cnn_ray_tune pid=2936977)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=2936977)[0m │ dropout_3 (Dropout)             │ (None, 3, 32)          │             0 │
[36m(train_cnn_ray_tune pid=2936977)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=2936977)[0m │ global_average_pooling1d        │ (None, 32)             │             0 │
[36m(train_cnn_ray_tune pid=2936977)[0m │ (GlobalAveragePooling1D)        │                        │               │
[36m(train_cnn_ray_tune pid=2936977)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=2936977)[0m │ dropout_4 (Dropout)             │ (None, 32)             │             0 │
[36m(train_cnn_ray_tune pid=2936977)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=2936977)[0m │ dense (Dense)                   │ (None, 4)              │           132 │
[36m(train_cnn_ray_tune pid=2936977)[0m └─────────────────────────────────┴────────────────────────┴───────────────┘
[36m(train_cnn_ray_tune pid=2936977)[0m  Total params: 33,732 (131.77 KB)
[36m(train_cnn_ray_tune pid=2936977)[0m  Trainable params: 33,732 (131.77 KB)
[36m(train_cnn_ray_tune pid=2936977)[0m  Non-trainable params: 0 (0.00 B)
[36m(train_cnn_ray_tune pid=2936972)[0m  Total params: 325,508 (1.24 MB)
[36m(train_cnn_ray_tune pid=2936972)[0m  Trainable params: 325,508 (1.24 MB)
[36m(train_cnn_ray_tune pid=2936972)[0m Epoch 1/75
[36m(train_cnn_ray_tune pid=2936980)[0m 
[1m 1/82[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2:47[0m 2s/step - accuracy: 0.2578 - loss: 2.6236
[1m 3/82[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 27ms/step - accuracy: 0.2704 - loss: 2.5749
[36m(train_cnn_ray_tune pid=2936980)[0m 
[1m 6/82[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 25ms/step - accuracy: 0.2791 - loss: 2.5437
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[36m(train_cnn_ray_tune pid=2936973)[0m 
[1m  1/164[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:16[0m 3s/step - accuracy: 0.3125 - loss: 2.3518
[36m(train_cnn_ray_tune pid=2936980)[0m 
[1m11/82[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 24ms/step - accuracy: 0.2773 - loss: 2.5023
[1m13/82[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 24ms/step - accuracy: 0.2794 - loss: 2.4789
[36m(train_cnn_ray_tune pid=2936973)[0m 
[1m  4/164[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2757 - loss: 2.6693
[36m(train_cnn_ray_tune pid=2936980)[0m 
[1m15/82[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 25ms/step - accuracy: 0.2810 - loss: 2.4532
[1m17/82[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 25ms/step - accuracy: 0.2823 - loss: 2.4296
[36m(train_cnn_ray_tune pid=2936973)[0m 
[1m  7/164[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.2605 - loss: 2.7687
[1m 10/164[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.2602 - loss: 2.7739
[36m(train_cnn_ray_tune pid=2936980)[0m 
[1m20/82[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 24ms/step - accuracy: 0.2837 - loss: 2.3987
[36m(train_cnn_ray_tune pid=2936973)[0m 
[1m 13/164[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.2597 - loss: 2.7753
[36m(train_cnn_ray_tune pid=2936980)[0m 
[1m22/82[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 25ms/step - accuracy: 0.2843 - loss: 2.3787
[1m25/82[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 25ms/step - accuracy: 0.2851 - loss: 2.3510
[36m(train_cnn_ray_tune pid=2936973)[0m 
[1m 16/164[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2587 - loss: 2.7768
[36m(train_cnn_ray_tune pid=2936973)[0m 
[1m 18/164[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2577 - loss: 2.7823
[1m 21/164[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2570 - loss: 2.7866
[36m(train_cnn_ray_tune pid=2936980)[0m 
[1m28/82[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 25ms/step - accuracy: 0.2860 - loss: 2.3258
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[36m(train_cnn_ray_tune pid=2936973)[0m 
[1m 23/164[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2569 - loss: 2.7873
[1m 25/164[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2567 - loss: 2.7870
[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936973)[0m 
[1m 28/164[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2566 - loss: 2.7844
[36m(train_cnn_ray_tune pid=2936980)[0m 
[1m34/82[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 26ms/step - accuracy: 0.2871 - loss: 2.2807
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[36m(train_cnn_ray_tune pid=2936981)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
[1m48/82[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 28ms/step - accuracy: 0.2902 - loss: 2.1924
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[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936952)[0m 
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[1m  5/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 38ms/step - accuracy: 0.2909 - loss: 2.3329
[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936952)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
[1m56/82[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 30ms/step - accuracy: 0.2921 - loss: 2.1500
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[36m(train_cnn_ray_tune pid=2936952)[0m 
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[36m(train_cnn_ray_tune pid=2936952)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936952)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936952)[0m 
[1m 17/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 34ms/step - accuracy: 0.2782 - loss: 2.4586
[36m(train_cnn_ray_tune pid=2936952)[0m 
[1m 19/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 34ms/step - accuracy: 0.2778 - loss: 2.4631
[36m(train_cnn_ray_tune pid=2936980)[0m 
[1m64/82[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 32ms/step - accuracy: 0.2936 - loss: 2.1121
[1m66/82[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 32ms/step - accuracy: 0.2940 - loss: 2.1032
[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
[1m72/82[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 32ms/step - accuracy: 0.2948 - loss: 2.0779
[36m(train_cnn_ray_tune pid=2936975)[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=2936975)[0m ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2936975)[0m ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2936975)[0m ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2936975)[0m │ global_average_pooling1d        │ (None, 32)             │             0 │[32m [repeated 126x across cluster][0m
[36m(train_cnn_ray_tune pid=2936975)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤[32m [repeated 227x across cluster][0m
[36m(train_cnn_ray_tune pid=2936975)[0m │ layer_normalization             │ (None, 3, 32)          │            64 │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2936975)[0m │ (LayerNormalization)            │                        │               │[32m [repeated 63x across cluster][0m
[36m(train_cnn_ray_tune pid=2936975)[0m │ dropout (Dropout)               │ (None, 3, 32)          │             0 │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2936975)[0m │ dropout_4 (Dropout)             │ (None, 32)             │             0 │[32m [repeated 63x across cluster][0m
[36m(train_cnn_ray_tune pid=2936975)[0m │ (GlobalAveragePooling1D)        │                        │               │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2936975)[0m │ dense (Dense)                   │ (None, 4)              │           132 │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2936975)[0m └─────────────────────────────────┴────────────────────────┴───────────────┘[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2936975)[0m  Total params: 33,732 (131.77 KB)[32m [repeated 17x across cluster][0m
[36m(train_cnn_ray_tune pid=2936975)[0m  Trainable params: 33,732 (131.77 KB)[32m [repeated 17x across cluster][0m
[36m(train_cnn_ray_tune pid=2936975)[0m  Non-trainable params: 0 (0.00 B)[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2936976)[0m  Total params: 325,508 (1.24 MB)
[36m(train_cnn_ray_tune pid=2936976)[0m  Trainable params: 325,508 (1.24 MB)
[36m(train_cnn_ray_tune pid=2936980)[0m 
[1m74/82[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 32ms/step - accuracy: 0.2950 - loss: 2.0699
[1m76/82[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 33ms/step - accuracy: 0.2952 - loss: 2.0622
[36m(train_cnn_ray_tune pid=2936975)[0m Epoch 1/109[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2936980)[0m 
[1m78/82[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 33ms/step - accuracy: 0.2954 - loss: 2.0546
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[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936971)[0m 
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[36m(train_cnn_ray_tune pid=2936960)[0m 
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[36m(train_cnn_ray_tune pid=2936959)[0m 
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[36m(train_cnn_ray_tune pid=2936978)[0m 
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[36m(train_cnn_ray_tune pid=2936975)[0m 
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[36m(train_cnn_ray_tune pid=2936978)[0m 
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[36m(train_cnn_ray_tune pid=2936979)[0m 
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[36m(train_cnn_ray_tune pid=2936976)[0m 
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[36m(train_cnn_ray_tune pid=2936971)[0m 
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[36m(train_cnn_ray_tune pid=2936978)[0m 
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[36m(train_cnn_ray_tune pid=2936973)[0m Epoch 2/109[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=2936974)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936977)[0m 
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[36m(train_cnn_ray_tune pid=2936960)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936977)[0m 
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[36m(train_cnn_ray_tune pid=2936959)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936977)[0m 
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[36m(train_cnn_ray_tune pid=2936978)[0m 
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[36m(train_cnn_ray_tune pid=2936978)[0m 
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[36m(train_cnn_ray_tune pid=2936959)[0m 
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[36m(train_cnn_ray_tune pid=2936978)[0m 
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[36m(train_cnn_ray_tune pid=2936946)[0m 
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[36m(train_cnn_ray_tune pid=2936977)[0m Epoch 3/69[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2936977)[0m 
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[36m(train_cnn_ray_tune pid=2936979)[0m 
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[36m(train_cnn_ray_tune pid=2936974)[0m 
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[36m(train_cnn_ray_tune pid=2936960)[0m 
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[36m(train_cnn_ray_tune pid=2936960)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936959)[0m 
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[36m(train_cnn_ray_tune pid=2936961)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936979)[0m 
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[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936975)[0m 
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[36m(train_cnn_ray_tune pid=2936959)[0m Epoch 3/85[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=2936962)[0m 
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[36m(train_cnn_ray_tune pid=2936960)[0m 
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[36m(train_cnn_ray_tune pid=2936952)[0m 
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[36m(train_cnn_ray_tune pid=2936977)[0m 
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[36m(train_cnn_ray_tune pid=2936975)[0m 
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[36m(train_cnn_ray_tune pid=2936946)[0m 
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[36m(train_cnn_ray_tune pid=2936975)[0m 
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[36m(train_cnn_ray_tune pid=2936978)[0m 
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[36m(train_cnn_ray_tune pid=2936976)[0m 
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[36m(train_cnn_ray_tune pid=2936973)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936960)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-05 10:35:03. 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_a2ecf    RUNNING            2   adam            relu                                   32                128                  5          0.00125802          53 │
│ trial_a2ecf    RUNNING            4   rmsprop         tanh                                   32                 64                  5          0.000458509         75 │
│ trial_a2ecf    RUNNING            3   adam            tanh                                   64                 64                  3          2.2792e-05          85 │
│ trial_a2ecf    RUNNING            4   adam            relu                                  128                 32                  3          0.000635588        123 │
│ trial_a2ecf    RUNNING            3   rmsprop         tanh                                   64                 32                  5          0.000121978         82 │
│ trial_a2ecf    RUNNING            4   adam            relu                                  128                128                  3          0.000315664         95 │
│ trial_a2ecf    RUNNING            2   adam            relu                                   64                128                  3          6.73958e-05        109 │
│ trial_a2ecf    RUNNING            3   adam            relu                                   32                128                  5          3.53159e-05        143 │
│ trial_a2ecf    RUNNING            2   rmsprop         tanh                                  128                128                  3          0.00302364         124 │
│ trial_a2ecf    RUNNING            4   adam            relu                                   32                 32                  5          0.00089052         105 │
│ trial_a2ecf    RUNNING            3   adam            tanh                                   32                 32                  3          1.24551e-05        117 │
│ trial_a2ecf    RUNNING            4   adam            tanh                                  128                 32                  3          0.000781525         69 │
│ trial_a2ecf    RUNNING            4   adam            tanh                                  128                128                  3          0.000165006         85 │
│ trial_a2ecf    RUNNING            3   rmsprop         relu                                   64                 32                  5          1.53927e-05        111 │
│ trial_a2ecf    RUNNING            4   adam            tanh                                   32                 32                  5          0.00375735         100 │
│ trial_a2ecf    RUNNING            4   adam            relu                                   64                 64                  5          0.00494679         109 │
│ trial_a2ecf    RUNNING            4   adam            relu                                  128                 32                  3          2.97458e-05        109 │
│ trial_a2ecf    RUNNING            3   adam            tanh                                   64                128                  5          0.000417164         75 │
│ trial_a2ecf    RUNNING            4   rmsprop         relu                                   32                128                  3          0.00366154         135 │
│ trial_a2ecf    RUNNING            3   rmsprop         relu                                   32                 64                  5          0.00102226          92 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2936974)[0m 
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[36m(train_cnn_ray_tune pid=2936970)[0m 
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[36m(train_cnn_ray_tune pid=2936978)[0m 
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[36m(train_cnn_ray_tune pid=2936961)[0m 
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[36m(train_cnn_ray_tune pid=2936961)[0m 
[1m 20/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 45ms/step - accuracy: 0.3094 - loss: 1.3920[32m [repeated 45x across cluster][0m
[36m(train_cnn_ray_tune pid=2936962)[0m Epoch 3/109[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=2936974)[0m 
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[36m(train_cnn_ray_tune pid=2936960)[0m 
[1m15/82[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 41ms/step - accuracy: 0.2574 - loss: 1.5699
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[36m(train_cnn_ray_tune pid=2936960)[0m 
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[36m(train_cnn_ray_tune pid=2936973)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936959)[0m 
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[36m(train_cnn_ray_tune pid=2936975)[0m 
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[36m(train_cnn_ray_tune pid=2936961)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936962)[0m 
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[36m(train_cnn_ray_tune pid=2936971)[0m 
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[36m(train_cnn_ray_tune pid=2936961)[0m 
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[36m(train_cnn_ray_tune pid=2936976)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936977)[0m 
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[36m(train_cnn_ray_tune pid=2936977)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936978)[0m 
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[36m(train_cnn_ray_tune pid=2936959)[0m 
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[36m(train_cnn_ray_tune pid=2936960)[0m 
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[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936981)[0m 
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[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936959)[0m 
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[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936970)[0m 
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[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936971)[0m Epoch 6/82[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=2936961)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936975)[0m 
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[36m(train_cnn_ray_tune pid=2936977)[0m 
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[36m(train_cnn_ray_tune pid=2936946)[0m 
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[36m(train_cnn_ray_tune pid=2936976)[0m 
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[36m(train_cnn_ray_tune pid=2936976)[0m - loss: 2.5900
[36m(train_cnn_ray_tune pid=2936975)[0m 
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[36m(train_cnn_ray_tune pid=2936959)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936977)[0m 
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[36m(train_cnn_ray_tune pid=2936978)[0m 
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[36m(train_cnn_ray_tune pid=2936970)[0m 
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[36m(train_cnn_ray_tune pid=2936978)[0m 
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[36m(train_cnn_ray_tune pid=2936978)[0m 
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[36m(train_cnn_ray_tune pid=2936978)[0m 
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[36m(train_cnn_ray_tune pid=2936975)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m Epoch 5/85[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936961)[0m 
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[36m(train_cnn_ray_tune pid=2936962)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936974)[0m 
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[36m(train_cnn_ray_tune pid=2936979)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936981)[0m 
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[36m(train_cnn_ray_tune pid=2936973)[0m 
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[36m(train_cnn_ray_tune pid=2936976)[0m 
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[36m(train_cnn_ray_tune pid=2936962)[0m 
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[36m(train_cnn_ray_tune pid=2936961)[0m 
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[36m(train_cnn_ray_tune pid=2936977)[0m 
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[36m(train_cnn_ray_tune pid=2936960)[0m 
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[36m(train_cnn_ray_tune pid=2936960)[0m 
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[36m(train_cnn_ray_tune pid=2936960)[0m 
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[36m(train_cnn_ray_tune pid=2936960)[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=2936960)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2936975)[0m 2025-11-05 10:34:37.043259: 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=2936961)[0m 2025-11-05 10:34:37.086001: 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=2936975)[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=2936975)[0m E0000 00:00:1762335277.094506 2938226 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=2936975)[0m E0000 00:00:1762335277.102895 2938226 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=2936975)[0m W0000 00:00:1762335277.122928 2938226 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=2936975)[0m 2025-11-05 10:34:37.129061: 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=2936975)[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=2936975)[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=2936975)[0m   warnings.warn([32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=2936962)[0m 2025-11-05 10:34:40.350698: 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=2936962)[0m 2025-11-05 10:34:40.350759: 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=2936962)[0m 2025-11-05 10:34:40.350769: 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=2936962)[0m 2025-11-05 10:34:40.350778: 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=2936962)[0m 2025-11-05 10:34:40.350786: 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=2936962)[0m 2025-11-05 10:34:40.350791: 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=2936962)[0m 2025-11-05 10:34:40.351245: 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=2936962)[0m 2025-11-05 10:34:40.351288: 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=2936962)[0m 2025-11-05 10:34:40.351292: 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=2936974)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936960)[0m 
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Trial trial_a2ecf finished iteration 1 at 2025-11-05 10:35:26. Total running time: 53s
╭──────────────────────────────────────╮
│ Trial trial_a2ecf result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             50.4591 │
│ time_total_s                 50.4591 │
│ training_iteration                 1 │
│ val_accuracy                 0.33462 │
╰──────────────────────────────────────╯

Trial trial_a2ecf completed after 1 iterations at 2025-11-05 10:35:26. Total running time: 53s
[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936981)[0m 
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[36m(train_cnn_ray_tune pid=2936959)[0m 
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[36m(train_cnn_ray_tune pid=2936975)[0m 
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[36m(train_cnn_ray_tune pid=2936970)[0m 
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[36m(train_cnn_ray_tune pid=2936973)[0m 
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[36m(train_cnn_ray_tune pid=2936979)[0m 
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[36m(train_cnn_ray_tune pid=2936979)[0m 
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[36m(train_cnn_ray_tune pid=2936970)[0m 
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[36m(train_cnn_ray_tune pid=2936975)[0m 
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[36m(train_cnn_ray_tune pid=2936979)[0m 
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[36m(train_cnn_ray_tune pid=2936952)[0m Epoch 4/75[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936979)[0m 
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[36m(train_cnn_ray_tune pid=2936959)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 294ms/step
[36m(train_cnn_ray_tune pid=2936974)[0m 
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[36m(train_cnn_ray_tune pid=2936959)[0m 
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[36m(train_cnn_ray_tune pid=2936959)[0m 
[1m13/49[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 14ms/step
[1m18/49[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=2936971)[0m 
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[36m(train_cnn_ray_tune pid=2936959)[0m 
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[36m(train_cnn_ray_tune pid=2936975)[0m 
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[36m(train_cnn_ray_tune pid=2936959)[0m 
[1m28/49[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 12ms/step
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[36m(train_cnn_ray_tune pid=2936959)[0m 
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[1m41/49[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=2936959)[0m 
[1m45/49[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936959)[0m 
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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 19ms/step
[36m(train_cnn_ray_tune pid=2936962)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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Trial status: 19 RUNNING | 1 TERMINATED
Current time: 2025-11-05 10:35:33. Total running time: 1min 0s
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_a2ecf    RUNNING              2   adam            relu                                   32                128                  5          0.00125802          53                                              │
│ trial_a2ecf    RUNNING              4   rmsprop         tanh                                   32                 64                  5          0.000458509         75                                              │
│ trial_a2ecf    RUNNING              3   adam            tanh                                   64                 64                  3          2.2792e-05          85                                              │
│ trial_a2ecf    RUNNING              3   rmsprop         tanh                                   64                 32                  5          0.000121978         82                                              │
│ trial_a2ecf    RUNNING              4   adam            relu                                  128                128                  3          0.000315664         95                                              │
│ trial_a2ecf    RUNNING              2   adam            relu                                   64                128                  3          6.73958e-05        109                                              │
│ trial_a2ecf    RUNNING              3   adam            relu                                   32                128                  5          3.53159e-05        143                                              │
│ trial_a2ecf    RUNNING              2   rmsprop         tanh                                  128                128                  3          0.00302364         124                                              │
│ trial_a2ecf    RUNNING              4   adam            relu                                   32                 32                  5          0.00089052         105                                              │
│ trial_a2ecf    RUNNING              3   adam            tanh                                   32                 32                  3          1.24551e-05        117                                              │
│ trial_a2ecf    RUNNING              4   adam            tanh                                  128                 32                  3          0.000781525         69                                              │
│ trial_a2ecf    RUNNING              4   adam            tanh                                  128                128                  3          0.000165006         85                                              │
│ trial_a2ecf    RUNNING              3   rmsprop         relu                                   64                 32                  5          1.53927e-05        111                                              │
│ trial_a2ecf    RUNNING              4   adam            tanh                                   32                 32                  5          0.00375735         100                                              │
│ trial_a2ecf    RUNNING              4   adam            relu                                   64                 64                  5          0.00494679         109                                              │
│ trial_a2ecf    RUNNING              4   adam            relu                                  128                 32                  3          2.97458e-05        109                                              │
│ trial_a2ecf    RUNNING              3   adam            tanh                                   64                128                  5          0.000417164         75                                              │
│ trial_a2ecf    RUNNING              4   rmsprop         relu                                   32                128                  3          0.00366154         135                                              │
│ trial_a2ecf    RUNNING              3   rmsprop         relu                                   32                 64                  5          0.00102226          92                                              │
│ trial_a2ecf    TERMINATED           4   adam            relu                                  128                 32                  3          0.000635588        123        1            50.4591         0.334621 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2936977)[0m 
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[36m(train_cnn_ray_tune pid=2936959)[0m 
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[1m 5/89[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 14ms/step
[36m(train_cnn_ray_tune pid=2936962)[0m 
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[36m(train_cnn_ray_tune pid=2936961)[0m 
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[36m(train_cnn_ray_tune pid=2936959)[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=2936959)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936978)[0m 
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[36m(train_cnn_ray_tune pid=2936959)[0m 
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[36m(train_cnn_ray_tune pid=2936959)[0m 
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[36m(train_cnn_ray_tune pid=2936978)[0m 
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[36m(train_cnn_ray_tune pid=2936971)[0m 
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[36m(train_cnn_ray_tune pid=2936978)[0m 
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Trial trial_a2ecf finished iteration 1 at 2025-11-05 10:35:35. Total running time: 1min 1s
╭──────────────────────────────────────╮
│ Trial trial_a2ecf result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             58.6993 │
│ time_total_s                 58.6993 │
│ training_iteration                 1 │
│ val_accuracy                 0.38834 │
╰──────────────────────────────────────╯

Trial trial_a2ecf completed after 1 iterations at 2025-11-05 10:35:35. Total running time: 1min 1s
[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936974)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936946)[0m 
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[36m(train_cnn_ray_tune pid=2936977)[0m 
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[36m(train_cnn_ray_tune pid=2936977)[0m 
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[36m(train_cnn_ray_tune pid=2936977)[0m 
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[36m(train_cnn_ray_tune pid=2936977)[0m 
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[36m(train_cnn_ray_tune pid=2936977)[0m 
[1m45/49[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 16ms/step
[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936961)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936977)[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=2936977)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2936977)[0m 
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[36m(train_cnn_ray_tune pid=2936977)[0m 
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Trial trial_a2ecf finished iteration 1 at 2025-11-05 10:35:40. Total running time: 1min 7s
╭──────────────────────────────────────╮
│ Trial trial_a2ecf result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             64.1126 │
│ time_total_s                 64.1126 │
│ training_iteration                 1 │
│ val_accuracy                  0.3743 │
╰──────────────────────────────────────╯

Trial trial_a2ecf completed after 1 iterations at 2025-11-05 10:35:40. Total running time: 1min 7s
[36m(train_cnn_ray_tune pid=2936979)[0m Epoch 5/117[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936971)[0m 
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[36m(train_cnn_ray_tune pid=2936979)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936971)[0m 
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[36m(train_cnn_ray_tune pid=2936952)[0m 
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[36m(train_cnn_ray_tune pid=2936952)[0m 
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[36m(train_cnn_ray_tune pid=2936975)[0m 
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[36m(train_cnn_ray_tune pid=2936962)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936976)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936981)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936981)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936962)[0m 
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[36m(train_cnn_ray_tune pid=2936961)[0m 
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[36m(train_cnn_ray_tune pid=2936979)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936975)[0m 
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[36m(train_cnn_ray_tune pid=2936981)[0m 
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[36m(train_cnn_ray_tune pid=2936973)[0m 
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[36m(train_cnn_ray_tune pid=2936973)[0m 
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[36m(train_cnn_ray_tune pid=2936946)[0m 
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[36m(train_cnn_ray_tune pid=2936946)[0m Epoch 10/95[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2936981)[0m 
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[36m(train_cnn_ray_tune pid=2936975)[0m 
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[36m(train_cnn_ray_tune pid=2936971)[0m 
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[36m(train_cnn_ray_tune pid=2936976)[0m 
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[36m(train_cnn_ray_tune pid=2936975)[0m 
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[36m(train_cnn_ray_tune pid=2936976)[0m 
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[36m(train_cnn_ray_tune pid=2936970)[0m 
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[36m(train_cnn_ray_tune pid=2936979)[0m 
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[36m(train_cnn_ray_tune pid=2936974)[0m 
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[36m(train_cnn_ray_tune pid=2936975)[0m 
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[36m(train_cnn_ray_tune pid=2936975)[0m 
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[36m(train_cnn_ray_tune pid=2936961)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936961)[0m 
[1m 25/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 37ms/step - accuracy: 0.4579 - loss: 1.1876[32m [repeated 17x across cluster][0m
[36m(train_cnn_ray_tune pid=2936980)[0m Epoch 17/124[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=2936952)[0m 
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[1m  6/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 26ms/step - accuracy: 0.3219 - loss: 1.3676 
[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936974)[0m 
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[36m(train_cnn_ray_tune pid=2936975)[0m 
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[36m(train_cnn_ray_tune pid=2936978)[0m 
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[36m(train_cnn_ray_tune pid=2936975)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936962)[0m 
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[36m(train_cnn_ray_tune pid=2936979)[0m 
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[36m(train_cnn_ray_tune pid=2936962)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936975)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[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=2936980)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2936981)[0m 
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[36m(train_cnn_ray_tune pid=2936981)[0m 
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[36m(train_cnn_ray_tune pid=2936952)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936979)[0m Epoch 7/117[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936980)[0m 
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Trial trial_a2ecf finished iteration 1 at 2025-11-05 10:36:02. Total running time: 1min 28s
╭──────────────────────────────────────╮
│ Trial trial_a2ecf result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             85.7329 │
│ time_total_s                 85.7329 │
│ training_iteration                 1 │
│ val_accuracy                 0.49263 │
╰──────────────────────────────────────╯

Trial trial_a2ecf completed after 1 iterations at 2025-11-05 10:36:02. Total running time: 1min 28s
[36m(train_cnn_ray_tune pid=2936980)[0m 
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[36m(train_cnn_ray_tune pid=2936981)[0m 
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[36m(train_cnn_ray_tune pid=2936978)[0m 
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[36m(train_cnn_ray_tune pid=2936946)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936975)[0m 
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Trial status: 16 RUNNING | 4 TERMINATED
Current time: 2025-11-05 10:36:03. Total running time: 1min 30s
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     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_a2ecf    RUNNING              2   adam            relu                                   32                128                  5          0.00125802          53                                              │
│ trial_a2ecf    RUNNING              4   rmsprop         tanh                                   32                 64                  5          0.000458509         75                                              │
│ trial_a2ecf    RUNNING              3   rmsprop         tanh                                   64                 32                  5          0.000121978         82                                              │
│ trial_a2ecf    RUNNING              4   adam            relu                                  128                128                  3          0.000315664         95                                              │
│ trial_a2ecf    RUNNING              2   adam            relu                                   64                128                  3          6.73958e-05        109                                              │
│ trial_a2ecf    RUNNING              3   adam            relu                                   32                128                  5          3.53159e-05        143                                              │
│ trial_a2ecf    RUNNING              4   adam            relu                                   32                 32                  5          0.00089052         105                                              │
│ trial_a2ecf    RUNNING              3   adam            tanh                                   32                 32                  3          1.24551e-05        117                                              │
│ trial_a2ecf    RUNNING              4   adam            tanh                                  128                128                  3          0.000165006         85                                              │
│ trial_a2ecf    RUNNING              3   rmsprop         relu                                   64                 32                  5          1.53927e-05        111                                              │
│ trial_a2ecf    RUNNING              4   adam            tanh                                   32                 32                  5          0.00375735         100                                              │
│ trial_a2ecf    RUNNING              4   adam            relu                                   64                 64                  5          0.00494679         109                                              │
│ trial_a2ecf    RUNNING              4   adam            relu                                  128                 32                  3          2.97458e-05        109                                              │
│ trial_a2ecf    RUNNING              3   adam            tanh                                   64                128                  5          0.000417164         75                                              │
│ trial_a2ecf    RUNNING              4   rmsprop         relu                                   32                128                  3          0.00366154         135                                              │
│ trial_a2ecf    RUNNING              3   rmsprop         relu                                   32                 64                  5          0.00102226          92                                              │
│ trial_a2ecf    TERMINATED           3   adam            tanh                                   64                 64                  3          2.2792e-05          85        1            58.6993         0.388343 │
│ trial_a2ecf    TERMINATED           4   adam            relu                                  128                 32                  3          0.000635588        123        1            50.4591         0.334621 │
│ trial_a2ecf    TERMINATED           2   rmsprop         tanh                                  128                128                  3          0.00302364         124        1            85.7329         0.492626 │
│ trial_a2ecf    TERMINATED           4   adam            tanh                                  128                 32                  3          0.000781525         69        1            64.1126         0.374298 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2936979)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936975)[0m 
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[36m(train_cnn_ray_tune pid=2936962)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936970)[0m 
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[36m(train_cnn_ray_tune pid=2936970)[0m 
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[36m(train_cnn_ray_tune pid=2936971)[0m 
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[36m(train_cnn_ray_tune pid=2936970)[0m Epoch 7/100[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2936946)[0m 
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[36m(train_cnn_ray_tune pid=2936946)[0m 
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[36m(train_cnn_ray_tune pid=2936971)[0m 
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[36m(train_cnn_ray_tune pid=2936976)[0m 
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[36m(train_cnn_ray_tune pid=2936976)[0m 
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[36m(train_cnn_ray_tune pid=2936974)[0m 
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[36m(train_cnn_ray_tune pid=2936946)[0m 
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[36m(train_cnn_ray_tune pid=2936946)[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=2936946)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936974)[0m 
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Trial trial_a2ecf finished iteration 1 at 2025-11-05 10:36:09. Total running time: 1min 36s
╭──────────────────────────────────────╮
│ Trial trial_a2ecf result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             93.1895 │
│ time_total_s                 93.1895 │
│ training_iteration                 1 │
│ val_accuracy                 0.37149 │
╰──────────────────────────────────────╯

Trial trial_a2ecf completed after 1 iterations at 2025-11-05 10:36:09. Total running time: 1min 36s
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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Trial trial_a2ecf finished iteration 1 at 2025-11-05 10:36:11. Total running time: 1min 37s
╭──────────────────────────────────────╮
│ Trial trial_a2ecf result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             94.5107 │
│ time_total_s                 94.5107 │
│ training_iteration                 1 │
│ val_accuracy                 0.36868 │
╰──────────────────────────────────────╯

Trial trial_a2ecf completed after 1 iterations at 2025-11-05 10:36:11. Total running time: 1min 37s
[36m(train_cnn_ray_tune pid=2936975)[0m Epoch 21/109[32m [repeated 13x across cluster][0m
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[36m(train_cnn_ray_tune pid=2936962)[0m 
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[36m(train_cnn_ray_tune pid=2936975)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936979)[0m 
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[36m(train_cnn_ray_tune pid=2936979)[0m 
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[36m(train_cnn_ray_tune pid=2936975)[0m 
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[36m(train_cnn_ray_tune pid=2936970)[0m 
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[36m(train_cnn_ray_tune pid=2936971)[0m Epoch 17/82[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936971)[0m 
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[36m(train_cnn_ray_tune pid=2936975)[0m 
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[36m(train_cnn_ray_tune pid=2936979)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 264ms/step
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[36m(train_cnn_ray_tune pid=2936975)[0m 
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[36m(train_cnn_ray_tune pid=2936979)[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[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=2936979)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936979)[0m 
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[36m(train_cnn_ray_tune pid=2936979)[0m 
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[36m(train_cnn_ray_tune pid=2936979)[0m 
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[36m(train_cnn_ray_tune pid=2936971)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936979)[0m 
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[36m(train_cnn_ray_tune pid=2936976)[0m 
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[36m(train_cnn_ray_tune pid=2936979)[0m 
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[36m(train_cnn_ray_tune pid=2936979)[0m 
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[36m(train_cnn_ray_tune pid=2936979)[0m 
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[36m(train_cnn_ray_tune pid=2936979)[0m 
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[36m(train_cnn_ray_tune pid=2936979)[0m 
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[36m(train_cnn_ray_tune pid=2936979)[0m 
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[36m(train_cnn_ray_tune pid=2936979)[0m 
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[36m(train_cnn_ray_tune pid=2936961)[0m 
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[36m(train_cnn_ray_tune pid=2936971)[0m 
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[36m(train_cnn_ray_tune pid=2936971)[0m 
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Trial trial_a2ecf finished iteration 1 at 2025-11-05 10:36:20. Total running time: 1min 47s
╭──────────────────────────────────────╮
│ Trial trial_a2ecf result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             104.193 │
│ time_total_s                 104.193 │
│ training_iteration                 1 │
│ val_accuracy                 0.36166 │
╰──────────────────────────────────────╯

Trial trial_a2ecf completed after 1 iterations at 2025-11-05 10:36:20. Total running time: 1min 47s
[36m(train_cnn_ray_tune pid=2936979)[0m 
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[36m(train_cnn_ray_tune pid=2936971)[0m 
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[36m(train_cnn_ray_tune pid=2936974)[0m Epoch 18/111[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=2936971)[0m 
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Trial trial_a2ecf finished iteration 1 at 2025-11-05 10:36:22. Total running time: 1min 48s
╭──────────────────────────────────────╮
│ Trial trial_a2ecf result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             105.634 │
│ time_total_s                 105.634 │
│ training_iteration                 1 │
│ val_accuracy                  0.4389 │
╰──────────────────────────────────────╯

Trial trial_a2ecf completed after 1 iterations at 2025-11-05 10:36:22. Total running time: 1min 48s
[36m(train_cnn_ray_tune pid=2936975)[0m 
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[36m(train_cnn_ray_tune pid=2936952)[0m 
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[36m(train_cnn_ray_tune pid=2936973)[0m 
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[36m(train_cnn_ray_tune pid=2936961)[0m 
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[36m(train_cnn_ray_tune pid=2936973)[0m 
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[36m(train_cnn_ray_tune pid=2936975)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936975)[0m 
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[36m(train_cnn_ray_tune pid=2936973)[0m 
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[36m(train_cnn_ray_tune pid=2936973)[0m 
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[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936952)[0m 
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[36m(train_cnn_ray_tune pid=2936975)[0m 
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[36m(train_cnn_ray_tune pid=2936970)[0m 
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Trial trial_a2ecf finished iteration 1 at 2025-11-05 10:36:24. Total running time: 1min 51s
╭──────────────────────────────────────╮
│ Trial trial_a2ecf result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             108.258 │
│ time_total_s                 108.258 │
│ training_iteration                 1 │
│ val_accuracy                  0.4856 │
╰──────────────────────────────────────╯

Trial trial_a2ecf completed after 1 iterations at 2025-11-05 10:36:24. Total running time: 1min 51s
[36m(train_cnn_ray_tune pid=2936975)[0m 
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[1m 8/49[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 8ms/step   
[36m(train_cnn_ray_tune pid=2936975)[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[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=2936975)[0m   _log_deprecation_warning([32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=2936974)[0m 
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[36m(train_cnn_ray_tune pid=2936975)[0m 
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[36m(train_cnn_ray_tune pid=2936975)[0m 
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[36m(train_cnn_ray_tune pid=2936981)[0m Epoch 10/92[32m [repeated 8x across cluster][0m

Trial trial_a2ecf finished iteration 1 at 2025-11-05 10:36:26. Total running time: 1min 53s
╭──────────────────────────────────────╮
│ Trial trial_a2ecf result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              110.29 │
│ time_total_s                  110.29 │
│ training_iteration                 1 │
│ val_accuracy                 0.38624 │
╰──────────────────────────────────────╯

Trial trial_a2ecf completed after 1 iterations at 2025-11-05 10:36:26. Total running time: 1min 53s
[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936975)[0m 
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[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936952)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936974)[0m Epoch 21/111[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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Trial status: 10 RUNNING | 10 TERMINATED
Current time: 2025-11-05 10:36:33. Total running time: 2min 0s
Logical resource usage: 10.0/20 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_a2ecf    RUNNING              2   adam            relu                                   32                128                  5          0.00125802          53                                              │
│ trial_a2ecf    RUNNING              4   rmsprop         tanh                                   32                 64                  5          0.000458509         75                                              │
│ trial_a2ecf    RUNNING              3   adam            relu                                   32                128                  5          3.53159e-05        143                                              │
│ trial_a2ecf    RUNNING              4   adam            tanh                                  128                128                  3          0.000165006         85                                              │
│ trial_a2ecf    RUNNING              3   rmsprop         relu                                   64                 32                  5          1.53927e-05        111                                              │
│ trial_a2ecf    RUNNING              4   adam            tanh                                   32                 32                  5          0.00375735         100                                              │
│ trial_a2ecf    RUNNING              4   adam            relu                                   64                 64                  5          0.00494679         109                                              │
│ trial_a2ecf    RUNNING              3   adam            tanh                                   64                128                  5          0.000417164         75                                              │
│ trial_a2ecf    RUNNING              4   rmsprop         relu                                   32                128                  3          0.00366154         135                                              │
│ trial_a2ecf    RUNNING              3   rmsprop         relu                                   32                 64                  5          0.00102226          92                                              │
│ trial_a2ecf    TERMINATED           3   adam            tanh                                   64                 64                  3          2.2792e-05          85        1            58.6993         0.388343 │
│ trial_a2ecf    TERMINATED           4   adam            relu                                  128                 32                  3          0.000635588        123        1            50.4591         0.334621 │
│ trial_a2ecf    TERMINATED           3   rmsprop         tanh                                   64                 32                  5          0.000121978         82        1           105.634          0.438904 │
│ trial_a2ecf    TERMINATED           4   adam            relu                                  128                128                  3          0.000315664         95        1            93.1895         0.371489 │
│ trial_a2ecf    TERMINATED           2   adam            relu                                   64                128                  3          6.73958e-05        109        1           108.258          0.485604 │
│ trial_a2ecf    TERMINATED           2   rmsprop         tanh                                  128                128                  3          0.00302364         124        1            85.7329         0.492626 │
│ trial_a2ecf    TERMINATED           4   adam            relu                                   32                 32                  5          0.00089052         105        1            94.5107         0.36868  │
│ trial_a2ecf    TERMINATED           3   adam            tanh                                   32                 32                  3          1.24551e-05        117        1           104.193          0.361657 │
│ trial_a2ecf    TERMINATED           4   adam            tanh                                  128                 32                  3          0.000781525         69        1            64.1126         0.374298 │
│ trial_a2ecf    TERMINATED           4   adam            relu                                  128                 32                  3          2.97458e-05        109        1           110.29           0.386236 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936970)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936974)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936976)[0m Epoch 9/143[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=2936981)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936974)[0m 
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[36m(train_cnn_ray_tune pid=2936976)[0m 
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[36m(train_cnn_ray_tune pid=2936961)[0m 
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[36m(train_cnn_ray_tune pid=2936952)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936981)[0m 
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[36m(train_cnn_ray_tune pid=2936974)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936945)[0m Epoch 12/53[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936974)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936970)[0m 
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[36m(train_cnn_ray_tune pid=2936981)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936962)[0m 
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[36m(train_cnn_ray_tune pid=2936952)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936962)[0m 
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[36m(train_cnn_ray_tune pid=2936961)[0m 
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[36m(train_cnn_ray_tune pid=2936974)[0m Epoch 26/111[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=2936961)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936962)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936962)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936970)[0m 
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[36m(train_cnn_ray_tune pid=2936981)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m Epoch 18/75[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=2936981)[0m 
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[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936962)[0m 
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[36m(train_cnn_ray_tune pid=2936952)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936974)[0m 
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[36m(train_cnn_ray_tune pid=2936962)[0m 
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[36m(train_cnn_ray_tune pid=2936962)[0m Epoch 20/109[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2936962)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936952)[0m 
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[36m(train_cnn_ray_tune pid=2936981)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936961)[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=2936961)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936952)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936976)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936962)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936974)[0m Epoch 31/111[32m [repeated 10x across cluster][0m
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[36m(train_cnn_ray_tune pid=2936961)[0m 
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[36m(train_cnn_ray_tune pid=2936961)[0m 
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[36m(train_cnn_ray_tune pid=2936961)[0m 
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[36m(train_cnn_ray_tune pid=2936961)[0m 
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[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936961)[0m 
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Trial trial_a2ecf finished iteration 1 at 2025-11-05 10:37:03. Total running time: 2min 30s
╭──────────────────────────────────────╮
│ Trial trial_a2ecf result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             146.898 │
│ time_total_s                 146.898 │
│ training_iteration                 1 │
│ val_accuracy                 0.50597 │
╰──────────────────────────────────────╯

Trial trial_a2ecf completed after 1 iterations at 2025-11-05 10:37:03. Total running time: 2min 30s
[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936952)[0m 
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[36m(train_cnn_ray_tune pid=2936970)[0m 
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[36m(train_cnn_ray_tune pid=2936970)[0m 
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Trial status: 9 RUNNING | 11 TERMINATED
Current time: 2025-11-05 10:37:03. Total running time: 2min 30s
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_a2ecf    RUNNING              2   adam            relu                                   32                128                  5          0.00125802          53                                              │
│ trial_a2ecf    RUNNING              4   rmsprop         tanh                                   32                 64                  5          0.000458509         75                                              │
│ trial_a2ecf    RUNNING              3   adam            relu                                   32                128                  5          3.53159e-05        143                                              │
│ trial_a2ecf    RUNNING              4   adam            tanh                                  128                128                  3          0.000165006         85                                              │
│ trial_a2ecf    RUNNING              3   rmsprop         relu                                   64                 32                  5          1.53927e-05        111                                              │
│ trial_a2ecf    RUNNING              4   adam            tanh                                   32                 32                  5          0.00375735         100                                              │
│ trial_a2ecf    RUNNING              4   adam            relu                                   64                 64                  5          0.00494679         109                                              │
│ trial_a2ecf    RUNNING              3   adam            tanh                                   64                128                  5          0.000417164         75                                              │
│ trial_a2ecf    RUNNING              3   rmsprop         relu                                   32                 64                  5          0.00102226          92                                              │
│ trial_a2ecf    TERMINATED           3   adam            tanh                                   64                 64                  3          2.2792e-05          85        1            58.6993         0.388343 │
│ trial_a2ecf    TERMINATED           4   adam            relu                                  128                 32                  3          0.000635588        123        1            50.4591         0.334621 │
│ trial_a2ecf    TERMINATED           3   rmsprop         tanh                                   64                 32                  5          0.000121978         82        1           105.634          0.438904 │
│ trial_a2ecf    TERMINATED           4   adam            relu                                  128                128                  3          0.000315664         95        1            93.1895         0.371489 │
│ trial_a2ecf    TERMINATED           2   adam            relu                                   64                128                  3          6.73958e-05        109        1           108.258          0.485604 │
│ trial_a2ecf    TERMINATED           2   rmsprop         tanh                                  128                128                  3          0.00302364         124        1            85.7329         0.492626 │
│ trial_a2ecf    TERMINATED           4   adam            relu                                   32                 32                  5          0.00089052         105        1            94.5107         0.36868  │
│ trial_a2ecf    TERMINATED           3   adam            tanh                                   32                 32                  3          1.24551e-05        117        1           104.193          0.361657 │
│ trial_a2ecf    TERMINATED           4   adam            tanh                                  128                 32                  3          0.000781525         69        1            64.1126         0.374298 │
│ trial_a2ecf    TERMINATED           4   adam            relu                                  128                 32                  3          2.97458e-05        109        1           110.29           0.386236 │
│ trial_a2ecf    TERMINATED           4   rmsprop         relu                                   32                128                  3          0.00366154         135        1           146.898          0.505969 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2936970)[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=2936970)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2936970)[0m 
[1m 9/89[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 7ms/step 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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Trial trial_a2ecf finished iteration 1 at 2025-11-05 10:37:04. Total running time: 2min 31s
╭──────────────────────────────────────╮
│ Trial trial_a2ecf result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             148.161 │
│ time_total_s                 148.161 │
│ training_iteration                 1 │
│ val_accuracy                 0.38062 │
╰──────────────────────────────────────╯

Trial trial_a2ecf completed after 1 iterations at 2025-11-05 10:37:04. Total running time: 2min 31s
[36m(train_cnn_ray_tune pid=2936962)[0m 
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[36m(train_cnn_ray_tune pid=2936976)[0m 
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[36m(train_cnn_ray_tune pid=2936952)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936976)[0m 
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[36m(train_cnn_ray_tune pid=2936976)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936974)[0m Epoch 33/111[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=2936970)[0m 
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[36m(train_cnn_ray_tune pid=2936981)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936952)[0m 
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[36m(train_cnn_ray_tune pid=2936952)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m Epoch 23/75[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=2936981)[0m 
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[36m(train_cnn_ray_tune pid=2936962)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936952)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936974)[0m Epoch 37/111[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2936976)[0m 
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[36m(train_cnn_ray_tune pid=2936981)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936981)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936976)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[0m 
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[36m(train_cnn_ray_tune pid=2936958)[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=2936958)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2936958)[0m 
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Trial trial_a2ecf finished iteration 1 at 2025-11-05 10:37:20. Total running time: 2min 47s
╭──────────────────────────────────────╮
│ Trial trial_a2ecf result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             164.064 │
│ time_total_s                 164.064 │
│ training_iteration                 1 │
│ val_accuracy                  0.4361 │
╰──────────────────────────────────────╯

Trial trial_a2ecf completed after 1 iterations at 2025-11-05 10:37:20. Total running time: 2min 47s
[36m(train_cnn_ray_tune pid=2936962)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m Epoch 26/75[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=2936976)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936974)[0m 
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[36m(train_cnn_ray_tune pid=2936962)[0m 
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[36m(train_cnn_ray_tune pid=2936974)[0m 
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[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936952)[0m 
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[36m(train_cnn_ray_tune pid=2936974)[0m 
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[36m(train_cnn_ray_tune pid=2936981)[0m Epoch 21/92[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=2936974)[0m 
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[36m(train_cnn_ray_tune pid=2936974)[0m 
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[36m(train_cnn_ray_tune pid=2936981)[0m 
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[36m(train_cnn_ray_tune pid=2936974)[0m 
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[36m(train_cnn_ray_tune pid=2936962)[0m 
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[36m(train_cnn_ray_tune pid=2936952)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936962)[0m 
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[36m(train_cnn_ray_tune pid=2936981)[0m 
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[36m(train_cnn_ray_tune pid=2936962)[0m Epoch 31/109[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=2936981)[0m 
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[36m(train_cnn_ray_tune pid=2936974)[0m 
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[36m(train_cnn_ray_tune pid=2936974)[0m 
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Trial status: 7 RUNNING | 13 TERMINATED
Current time: 2025-11-05 10:37:33. Total running time: 3min 0s
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_a2ecf    RUNNING              2   adam            relu                                   32                128                  5          0.00125802          53                                              │
│ trial_a2ecf    RUNNING              4   rmsprop         tanh                                   32                 64                  5          0.000458509         75                                              │
│ trial_a2ecf    RUNNING              3   adam            relu                                   32                128                  5          3.53159e-05        143                                              │
│ trial_a2ecf    RUNNING              3   rmsprop         relu                                   64                 32                  5          1.53927e-05        111                                              │
│ trial_a2ecf    RUNNING              4   adam            relu                                   64                 64                  5          0.00494679         109                                              │
│ trial_a2ecf    RUNNING              3   adam            tanh                                   64                128                  5          0.000417164         75                                              │
│ trial_a2ecf    RUNNING              3   rmsprop         relu                                   32                 64                  5          0.00102226          92                                              │
│ trial_a2ecf    TERMINATED           3   adam            tanh                                   64                 64                  3          2.2792e-05          85        1            58.6993         0.388343 │
│ trial_a2ecf    TERMINATED           4   adam            relu                                  128                 32                  3          0.000635588        123        1            50.4591         0.334621 │
│ trial_a2ecf    TERMINATED           3   rmsprop         tanh                                   64                 32                  5          0.000121978         82        1           105.634          0.438904 │
│ trial_a2ecf    TERMINATED           4   adam            relu                                  128                128                  3          0.000315664         95        1            93.1895         0.371489 │
│ trial_a2ecf    TERMINATED           2   adam            relu                                   64                128                  3          6.73958e-05        109        1           108.258          0.485604 │
│ trial_a2ecf    TERMINATED           2   rmsprop         tanh                                  128                128                  3          0.00302364         124        1            85.7329         0.492626 │
│ trial_a2ecf    TERMINATED           4   adam            relu                                   32                 32                  5          0.00089052         105        1            94.5107         0.36868  │
│ trial_a2ecf    TERMINATED           3   adam            tanh                                   32                 32                  3          1.24551e-05        117        1           104.193          0.361657 │
│ trial_a2ecf    TERMINATED           4   adam            tanh                                  128                 32                  3          0.000781525         69        1            64.1126         0.374298 │
│ trial_a2ecf    TERMINATED           4   adam            tanh                                  128                128                  3          0.000165006         85        1           164.064          0.436096 │
│ trial_a2ecf    TERMINATED           4   adam            tanh                                   32                 32                  5          0.00375735         100        1           148.16           0.380618 │
│ trial_a2ecf    TERMINATED           4   adam            relu                                  128                 32                  3          2.97458e-05        109        1           110.29           0.386236 │
│ trial_a2ecf    TERMINATED           4   rmsprop         relu                                   32                128                  3          0.00366154         135        1           146.898          0.505969 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2936974)[0m 
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[36m(train_cnn_ray_tune pid=2936974)[0m 
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[36m(train_cnn_ray_tune pid=2936974)[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=2936974)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2936974)[0m 
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Trial trial_a2ecf finished iteration 1 at 2025-11-05 10:37:34. Total running time: 3min 1s
╭──────────────────────────────────────╮
│ Trial trial_a2ecf result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             178.187 │
│ time_total_s                 178.187 │
│ training_iteration                 1 │
│ val_accuracy                 0.40239 │
╰──────────────────────────────────────╯

Trial trial_a2ecf completed after 1 iterations at 2025-11-05 10:37:34. Total running time: 3min 1s
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[36m(train_cnn_ray_tune pid=2936952)[0m 
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[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936981)[0m 
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[36m(train_cnn_ray_tune pid=2936976)[0m 
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[36m(train_cnn_ray_tune pid=2936976)[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=2936976)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2936981)[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=2936981)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936976)[0m 
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[36m(train_cnn_ray_tune pid=2936962)[0m 
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Trial trial_a2ecf finished iteration 1 at 2025-11-05 10:37:42. Total running time: 3min 9s
╭──────────────────────────────────────╮
│ Trial trial_a2ecf result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             186.432 │
│ time_total_s                 186.432 │
│ training_iteration                 1 │
│ val_accuracy                 0.46419 │
╰──────────────────────────────────────╯

Trial trial_a2ecf completed after 1 iterations at 2025-11-05 10:37:42. Total running time: 3min 9s

Trial trial_a2ecf finished iteration 1 at 2025-11-05 10:37:43. Total running time: 3min 9s
╭──────────────────────────────────────╮
│ Trial trial_a2ecf result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             186.701 │
│ time_total_s                 186.701 │
│ training_iteration                 1 │
│ val_accuracy                 0.52809 │
╰──────────────────────────────────────╯

Trial trial_a2ecf completed after 1 iterations at 2025-11-05 10:37:43. Total running time: 3min 9s
[36m(train_cnn_ray_tune pid=2936952)[0m 
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[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936962)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936981)[0m 
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[36m(train_cnn_ray_tune pid=2936962)[0m 
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[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936962)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936962)[0m 
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[36m(train_cnn_ray_tune pid=2936962)[0m Epoch 41/109[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936952)[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=2936952)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936952)[0m 
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[36m(train_cnn_ray_tune pid=2936962)[0m 
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[36m(train_cnn_ray_tune pid=2936952)[0m 
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[36m(train_cnn_ray_tune pid=2936952)[0m 
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Trial trial_a2ecf finished iteration 1 at 2025-11-05 10:37:55. Total running time: 3min 21s
╭──────────────────────────────────────╮
│ Trial trial_a2ecf result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             198.847 │
│ time_total_s                 198.847 │
│ training_iteration                 1 │
│ val_accuracy                 0.48139 │
╰──────────────────────────────────────╯

Trial trial_a2ecf completed after 1 iterations at 2025-11-05 10:37:55. Total running time: 3min 21s
[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936962)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m Epoch 42/75[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936962)[0m 
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[36m(train_cnn_ray_tune pid=2936962)[0m Epoch 48/109[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936962)[0m 
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Trial status: 3 RUNNING | 17 TERMINATED
Current time: 2025-11-05 10:38:03. Total running time: 3min 30s
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_a2ecf    RUNNING              2   adam            relu                                   32                128                  5          0.00125802          53                                              │
│ trial_a2ecf    RUNNING              4   adam            relu                                   64                 64                  5          0.00494679         109                                              │
│ trial_a2ecf    RUNNING              3   adam            tanh                                   64                128                  5          0.000417164         75                                              │
│ trial_a2ecf    TERMINATED           4   rmsprop         tanh                                   32                 64                  5          0.000458509         75        1           198.847          0.48139  │
│ trial_a2ecf    TERMINATED           3   adam            tanh                                   64                 64                  3          2.2792e-05          85        1            58.6993         0.388343 │
│ trial_a2ecf    TERMINATED           4   adam            relu                                  128                 32                  3          0.000635588        123        1            50.4591         0.334621 │
│ trial_a2ecf    TERMINATED           3   rmsprop         tanh                                   64                 32                  5          0.000121978         82        1           105.634          0.438904 │
│ trial_a2ecf    TERMINATED           4   adam            relu                                  128                128                  3          0.000315664         95        1            93.1895         0.371489 │
│ trial_a2ecf    TERMINATED           2   adam            relu                                   64                128                  3          6.73958e-05        109        1           108.258          0.485604 │
│ trial_a2ecf    TERMINATED           3   adam            relu                                   32                128                  5          3.53159e-05        143        1           186.432          0.464185 │
│ trial_a2ecf    TERMINATED           2   rmsprop         tanh                                  128                128                  3          0.00302364         124        1            85.7329         0.492626 │
│ trial_a2ecf    TERMINATED           4   adam            relu                                   32                 32                  5          0.00089052         105        1            94.5107         0.36868  │
│ trial_a2ecf    TERMINATED           3   adam            tanh                                   32                 32                  3          1.24551e-05        117        1           104.193          0.361657 │
│ trial_a2ecf    TERMINATED           4   adam            tanh                                  128                 32                  3          0.000781525         69        1            64.1126         0.374298 │
│ trial_a2ecf    TERMINATED           4   adam            tanh                                  128                128                  3          0.000165006         85        1           164.064          0.436096 │
│ trial_a2ecf    TERMINATED           3   rmsprop         relu                                   64                 32                  5          1.53927e-05        111        1           178.187          0.402388 │
│ trial_a2ecf    TERMINATED           4   adam            tanh                                   32                 32                  5          0.00375735         100        1           148.16           0.380618 │
│ trial_a2ecf    TERMINATED           4   adam            relu                                  128                 32                  3          2.97458e-05        109        1           110.29           0.386236 │
│ trial_a2ecf    TERMINATED           4   rmsprop         relu                                   32                128                  3          0.00366154         135        1           146.898          0.505969 │
│ trial_a2ecf    TERMINATED           3   rmsprop         relu                                   32                 64                  5          0.00102226          92        1           186.701          0.52809  │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=2936945)[0m 
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[36m(train_cnn_ray_tune pid=2936962)[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=2936962)[0m   _log_deprecation_warning(
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[36m(train_cnn_ray_tune pid=2936962)[0m 
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Trial trial_a2ecf finished iteration 1 at 2025-11-05 10:38:05. Total running time: 3min 32s
╭──────────────────────────────────────╮
│ Trial trial_a2ecf result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             209.193 │
│ time_total_s                 209.193 │
│ training_iteration                 1 │
│ val_accuracy                 0.52774 │
╰──────────────────────────────────────╯

Trial trial_a2ecf completed after 1 iterations at 2025-11-05 10:38:05. Total running time: 3min 32s
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Trial trial_a2ecf finished iteration 1 at 2025-11-05 10:38:07. Total running time: 3min 34s
╭──────────────────────────────────────╮
│ Trial trial_a2ecf result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             210.888 │
│ time_total_s                 210.888 │
│ training_iteration                 1 │
│ val_accuracy                 0.57514 │
╰──────────────────────────────────────╯

Trial trial_a2ecf completed after 1 iterations at 2025-11-05 10:38:07. Total running time: 3min 34s
[36m(train_cnn_ray_tune pid=2936972)[0m Epoch 49/75[32m [repeated 7x across cluster][0m
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[36m(train_cnn_ray_tune pid=2936972)[0m 
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[36m(train_cnn_ray_tune pid=2936972)[0m Epoch 55/75[32m [repeated 6x across cluster][0m
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2025-11-05 10:38:14,239	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_M/case_M_ESANN_acc_superclasses_CPA_METs/ESANN_hyperparameters_tuning' in 0.0068s.
/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 00:00:1762335494.372629 2935316 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
[36m(train_cnn_ray_tune pid=2936972)[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[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=2936972)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
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Trial trial_a2ecf finished iteration 1 at 2025-11-05 10:38:14. Total running time: 3min 40s
╭──────────────────────────────────────╮
│ Trial trial_a2ecf result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             217.964 │
│ time_total_s                 217.964 │
│ training_iteration                 1 │
│ val_accuracy                 0.49052 │
╰──────────────────────────────────────╯

Trial trial_a2ecf completed after 1 iterations at 2025-11-05 10:38:14. Total running time: 3min 40s

Trial status: 20 TERMINATED
Current time: 2025-11-05 10:38:14. Total running time: 3min 40s
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_a2ecf    TERMINATED           2   adam            relu                                   32                128                  5          0.00125802          53        1           210.888          0.57514  │
│ trial_a2ecf    TERMINATED           4   rmsprop         tanh                                   32                 64                  5          0.000458509         75        1           198.847          0.48139  │
│ trial_a2ecf    TERMINATED           3   adam            tanh                                   64                 64                  3          2.2792e-05          85        1            58.6993         0.388343 │
│ trial_a2ecf    TERMINATED           4   adam            relu                                  128                 32                  3          0.000635588        123        1            50.4591         0.334621 │
│ trial_a2ecf    TERMINATED           3   rmsprop         tanh                                   64                 32                  5          0.000121978         82        1           105.634          0.438904 │
│ trial_a2ecf    TERMINATED           4   adam            relu                                  128                128                  3          0.000315664         95        1            93.1895         0.371489 │
│ trial_a2ecf    TERMINATED           2   adam            relu                                   64                128                  3          6.73958e-05        109        1           108.258          0.485604 │
│ trial_a2ecf    TERMINATED           3   adam            relu                                   32                128                  5          3.53159e-05        143        1           186.432          0.464185 │
│ trial_a2ecf    TERMINATED           2   rmsprop         tanh                                  128                128                  3          0.00302364         124        1            85.7329         0.492626 │
│ trial_a2ecf    TERMINATED           4   adam            relu                                   32                 32                  5          0.00089052         105        1            94.5107         0.36868  │
│ trial_a2ecf    TERMINATED           3   adam            tanh                                   32                 32                  3          1.24551e-05        117        1           104.193          0.361657 │
│ trial_a2ecf    TERMINATED           4   adam            tanh                                  128                 32                  3          0.000781525         69        1            64.1126         0.374298 │
│ trial_a2ecf    TERMINATED           4   adam            tanh                                  128                128                  3          0.000165006         85        1           164.064          0.436096 │
│ trial_a2ecf    TERMINATED           3   rmsprop         relu                                   64                 32                  5          1.53927e-05        111        1           178.187          0.402388 │
│ trial_a2ecf    TERMINATED           4   adam            tanh                                   32                 32                  5          0.00375735         100        1           148.16           0.380618 │
│ trial_a2ecf    TERMINATED           4   adam            relu                                   64                 64                  5          0.00494679         109        1           209.193          0.527739 │
│ trial_a2ecf    TERMINATED           4   adam            relu                                  128                 32                  3          2.97458e-05        109        1           110.29           0.386236 │
│ trial_a2ecf    TERMINATED           3   adam            tanh                                   64                128                  5          0.000417164         75        1           217.964          0.49052  │
│ trial_a2ecf    TERMINATED           4   rmsprop         relu                                   32                128                  3          0.00366154         135        1           146.898          0.505969 │
│ trial_a2ecf    TERMINATED           3   rmsprop         relu                                   32                 64                  5          0.00102226          92        1           186.701          0.52809  │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Mejores hiperparámetros: {'N_capas': 2, 'optimizador': 'adam', 'funcion_activacion': 'relu', 'tamanho_minilote': 32, 'numero_filtros': 128, 'tamanho_filtro': 5, 'tasa_aprendizaje': 0.0012580154979073056, 'epochs': 53}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/53
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762335496.196122 2981755 service.cc:152] XLA service 0x7e2c90016e00 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762335496.196171 2981755 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:38:16.236091: 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 00:00:1762335496.411191 2981755 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762335498.643302 2981755 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 2/53

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Epoch 3/53

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[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3669 - loss: 1.3276
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Epoch 4/53

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[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4044 - loss: 1.2935
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Epoch 5/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4045 - loss: 1.2669 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4028 - loss: 1.2685
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[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4122 - loss: 1.2611
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[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4154 - loss: 1.2591
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4157 - loss: 1.2590
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4156 - loss: 1.2588
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4153 - loss: 1.2588 - val_accuracy: 0.4933 - val_loss: 1.1779
Epoch 6/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4159 - loss: 1.2646 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4228 - loss: 1.2549
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4216 - loss: 1.2536
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4212 - loss: 1.2529
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4210 - loss: 1.2519
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4212 - loss: 1.2505
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4220 - loss: 1.2487
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4230 - loss: 1.2471
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4234 - loss: 1.2466 - val_accuracy: 0.5291 - val_loss: 1.1628
Epoch 7/53

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4455 - loss: 1.2286 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4442 - loss: 1.2309
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4392 - loss: 1.2326
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4359 - loss: 1.2338
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[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4343 - loss: 1.2328
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4334 - loss: 1.2325
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4329 - loss: 1.2319
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Epoch 8/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4472 - loss: 1.2006 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4500 - loss: 1.2026
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4522 - loss: 1.2016
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4515 - loss: 1.2028
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4499 - loss: 1.2041
[1m223/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4493 - loss: 1.2049
[1m262/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4487 - loss: 1.2056
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4480 - loss: 1.2060
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4475 - loss: 1.2063 - val_accuracy: 0.5162 - val_loss: 1.1594
Epoch 9/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1645
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4747 - loss: 1.1862 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4579 - loss: 1.1949
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4506 - loss: 1.1982
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4458 - loss: 1.2010
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4434 - loss: 1.2030
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4425 - loss: 1.2046
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4419 - loss: 1.2051
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4417 - loss: 1.2051
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4416 - loss: 1.2050 - val_accuracy: 0.5088 - val_loss: 1.1477
Epoch 10/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.3212
[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4265 - loss: 1.2302 
[1m 66/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4317 - loss: 1.2203
[1m106/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4379 - loss: 1.2119
[1m141/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4418 - loss: 1.2070
[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4441 - loss: 1.2033
[1m221/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4453 - loss: 1.2015
[1m262/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4458 - loss: 1.2006
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4462 - loss: 1.2001
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4467 - loss: 1.1995 - val_accuracy: 0.5242 - val_loss: 1.1408
Epoch 11/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6250 - loss: 1.0954
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4804 - loss: 1.1449 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4699 - loss: 1.1632
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4647 - loss: 1.1733
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4628 - loss: 1.1770
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4616 - loss: 1.1789
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4607 - loss: 1.1803
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4604 - loss: 1.1809
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4602 - loss: 1.1813
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4600 - loss: 1.1815 - val_accuracy: 0.5463 - val_loss: 1.1311
Epoch 12/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3438 - loss: 1.2724
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5001 - loss: 1.1371 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4855 - loss: 1.1527
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4793 - loss: 1.1591
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4761 - loss: 1.1622
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4735 - loss: 1.1642
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4713 - loss: 1.1657
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4693 - loss: 1.1674
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4680 - loss: 1.1687
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4674 - loss: 1.1694 - val_accuracy: 0.5305 - val_loss: 1.1270
Epoch 13/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4719 - loss: 1.1623 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4680 - loss: 1.1642
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4666 - loss: 1.1656
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4665 - loss: 1.1666
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[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4656 - loss: 1.1676
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4652 - loss: 1.1680
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4650 - loss: 1.1684
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4649 - loss: 1.1686 - val_accuracy: 0.5246 - val_loss: 1.1283
Epoch 14/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4661 - loss: 1.1890 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4634 - loss: 1.1867
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4648 - loss: 1.1818
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4661 - loss: 1.1791
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4671 - loss: 1.1770
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4684 - loss: 1.1745
[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4696 - loss: 1.1726
[1m321/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4705 - loss: 1.1711
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4707 - loss: 1.1709 - val_accuracy: 0.5506 - val_loss: 1.1133
Epoch 15/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4930 - loss: 1.1362 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4882 - loss: 1.1452
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4838 - loss: 1.1499
[1m163/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4818 - loss: 1.1526
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[1m245/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4794 - loss: 1.1563
[1m284/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4788 - loss: 1.1570
[1m321/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4785 - loss: 1.1574
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4785 - loss: 1.1575 - val_accuracy: 0.5327 - val_loss: 1.1162
Epoch 16/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4755 - loss: 1.1590 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4734 - loss: 1.1554
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4724 - loss: 1.1540
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4739 - loss: 1.1521
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4750 - loss: 1.1505
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4756 - loss: 1.1496
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4756 - loss: 1.1492
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4755 - loss: 1.1490
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4754 - loss: 1.1489 - val_accuracy: 0.5291 - val_loss: 1.1141
Epoch 17/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4062 - loss: 1.2660
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4694 - loss: 1.1608 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4700 - loss: 1.1590
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4706 - loss: 1.1565
[1m161/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4701 - loss: 1.1557
[1m202/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4703 - loss: 1.1551
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4707 - loss: 1.1547
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4714 - loss: 1.1541
[1m317/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4723 - loss: 1.1536
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4726 - loss: 1.1535 - val_accuracy: 0.5372 - val_loss: 1.1152
Epoch 18/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4667 - loss: 1.1239 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4756 - loss: 1.1317
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4802 - loss: 1.1355
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4824 - loss: 1.1360
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4840 - loss: 1.1367
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4851 - loss: 1.1368
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4861 - loss: 1.1365
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4869 - loss: 1.1364
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4873 - loss: 1.1364 - val_accuracy: 0.5432 - val_loss: 1.1249
Epoch 19/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1228
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4949 - loss: 1.1069 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4985 - loss: 1.1107
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4978 - loss: 1.1154
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4965 - loss: 1.1176
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4953 - loss: 1.1190
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4944 - loss: 1.1200
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4937 - loss: 1.1214
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4931 - loss: 1.1228
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4927 - loss: 1.1235 - val_accuracy: 0.5520 - val_loss: 1.1100
Epoch 20/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4930 - loss: 1.1299 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4952 - loss: 1.1306
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4932 - loss: 1.1334
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4916 - loss: 1.1352
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4908 - loss: 1.1358
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4907 - loss: 1.1354
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4911 - loss: 1.1344
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4914 - loss: 1.1339
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4915 - loss: 1.1338 - val_accuracy: 0.5520 - val_loss: 1.1122
Epoch 21/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.5625 - loss: 1.2052
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4835 - loss: 1.1579 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4818 - loss: 1.1551
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4824 - loss: 1.1525
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4845 - loss: 1.1481
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4854 - loss: 1.1455
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4862 - loss: 1.1434
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4871 - loss: 1.1421
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4881 - loss: 1.1408
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4885 - loss: 1.1401 - val_accuracy: 0.5646 - val_loss: 1.1072
Epoch 22/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 27ms/step - accuracy: 0.3750 - loss: 1.2488
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4701 - loss: 1.1095 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4828 - loss: 1.1054
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4874 - loss: 1.1081
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4893 - loss: 1.1120
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4901 - loss: 1.1145
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4906 - loss: 1.1164
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4912 - loss: 1.1177
[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4917 - loss: 1.1187
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4918 - loss: 1.1188 - val_accuracy: 0.5660 - val_loss: 1.1104
Epoch 23/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4951 - loss: 1.1190 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4924 - loss: 1.1142
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4922 - loss: 1.1153
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4933 - loss: 1.1158
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4938 - loss: 1.1163
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4942 - loss: 1.1166
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4947 - loss: 1.1164
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4949 - loss: 1.1164
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4950 - loss: 1.1165 - val_accuracy: 0.5583 - val_loss: 1.0982
Epoch 24/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 1.0493
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5283 - loss: 1.0741 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5171 - loss: 1.0814
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5125 - loss: 1.0856
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5086 - loss: 1.0900
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5058 - loss: 1.0935
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5051 - loss: 1.0954
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5049 - loss: 1.0964
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5045 - loss: 1.0977
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5044 - loss: 1.0982 - val_accuracy: 0.5520 - val_loss: 1.0845
Epoch 25/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.2426
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4873 - loss: 1.1263 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4974 - loss: 1.1181
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5029 - loss: 1.1135
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5041 - loss: 1.1121
[1m200/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5047 - loss: 1.1113
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5048 - loss: 1.1118
[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5046 - loss: 1.1124
[1m319/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5043 - loss: 1.1127
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5043 - loss: 1.1128 - val_accuracy: 0.5671 - val_loss: 1.0904
Epoch 26/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.0560
[1m 42/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5169 - loss: 1.0831 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5228 - loss: 1.0797
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5210 - loss: 1.0825
[1m161/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5202 - loss: 1.0840
[1m202/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5198 - loss: 1.0855
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5190 - loss: 1.0873
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5180 - loss: 1.0892
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5172 - loss: 1.0909
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5169 - loss: 1.0915 - val_accuracy: 0.5709 - val_loss: 1.0941
Epoch 27/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.2154
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4927 - loss: 1.1226 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4989 - loss: 1.1152
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4995 - loss: 1.1162
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5007 - loss: 1.1157
[1m199/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5016 - loss: 1.1153
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5026 - loss: 1.1146
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5033 - loss: 1.1139
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5041 - loss: 1.1127
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5044 - loss: 1.1124 - val_accuracy: 0.5636 - val_loss: 1.0850
Epoch 28/53

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[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4717 - loss: 1.1155 
[1m 67/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4799 - loss: 1.1156
[1m105/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4861 - loss: 1.1126
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4892 - loss: 1.1095
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4914 - loss: 1.1077
[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4931 - loss: 1.1068
[1m263/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4948 - loss: 1.1059
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4964 - loss: 1.1051
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4973 - loss: 1.1046 - val_accuracy: 0.5709 - val_loss: 1.0783
Epoch 29/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 1.0190
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5409 - loss: 1.0830 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5241 - loss: 1.0913
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5176 - loss: 1.0943
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5153 - loss: 1.0965
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5145 - loss: 1.0974
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5152 - loss: 1.0969
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5157 - loss: 1.0969
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5161 - loss: 1.0969
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5162 - loss: 1.0968 - val_accuracy: 0.5544 - val_loss: 1.0913
Epoch 30/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1517
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5042 - loss: 1.0752 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5072 - loss: 1.0824
[1m109/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5095 - loss: 1.0857
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5115 - loss: 1.0870
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5125 - loss: 1.0878
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5135 - loss: 1.0883
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5144 - loss: 1.0885
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5149 - loss: 1.0889
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5150 - loss: 1.0891 - val_accuracy: 0.5737 - val_loss: 1.0807
Epoch 31/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.0671
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5200 - loss: 1.0712 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5186 - loss: 1.0742
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5149 - loss: 1.0776
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5136 - loss: 1.0805
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5136 - loss: 1.0816
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5133 - loss: 1.0829
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5127 - loss: 1.0843
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5120 - loss: 1.0855
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5118 - loss: 1.0859 - val_accuracy: 0.5787 - val_loss: 1.0765
Epoch 32/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.4688 - loss: 1.2317
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5331 - loss: 1.0985 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5296 - loss: 1.0930
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5288 - loss: 1.0887
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5283 - loss: 1.0869
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5272 - loss: 1.0868
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5264 - loss: 1.0865
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5258 - loss: 1.0866
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5254 - loss: 1.0865
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5252 - loss: 1.0866 - val_accuracy: 0.5853 - val_loss: 1.0782
Epoch 33/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4996 - loss: 1.1281 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5087 - loss: 1.1093
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5144 - loss: 1.1006
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5163 - loss: 1.0965
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5173 - loss: 1.0935
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5186 - loss: 1.0905
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5190 - loss: 1.0885
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5195 - loss: 1.0868
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5197 - loss: 1.0858 - val_accuracy: 0.5836 - val_loss: 1.0766
Epoch 34/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 1.0518
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5279 - loss: 1.1047 
[1m 71/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5255 - loss: 1.1047
[1m110/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5262 - loss: 1.0952
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5261 - loss: 1.0898
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5264 - loss: 1.0866
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5264 - loss: 1.0844
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5261 - loss: 1.0833
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5259 - loss: 1.0822
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5261 - loss: 1.0816 - val_accuracy: 0.5769 - val_loss: 1.0703
Epoch 35/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5551 - loss: 1.0005 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5511 - loss: 1.0214
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5491 - loss: 1.0303
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5461 - loss: 1.0365
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5433 - loss: 1.0427
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5412 - loss: 1.0469
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5393 - loss: 1.0506
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5381 - loss: 1.0530
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5375 - loss: 1.0542 - val_accuracy: 0.5818 - val_loss: 1.0882
Epoch 36/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.2743
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4955 - loss: 1.1176 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5062 - loss: 1.0951
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5102 - loss: 1.0862
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5126 - loss: 1.0829
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5149 - loss: 1.0809
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5161 - loss: 1.0800
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5171 - loss: 1.0792
[1m317/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5182 - loss: 1.0781
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5186 - loss: 1.0778 - val_accuracy: 0.5801 - val_loss: 1.0749
Epoch 37/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6250 - loss: 1.0661
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5354 - loss: 1.0913 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5319 - loss: 1.0828
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5296 - loss: 1.0792
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5277 - loss: 1.0779
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5273 - loss: 1.0771
[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5272 - loss: 1.0757
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5273 - loss: 1.0743
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5274 - loss: 1.0729
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5276 - loss: 1.0722 - val_accuracy: 0.5776 - val_loss: 1.0664
Epoch 38/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5245 - loss: 1.0755 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5358 - loss: 1.0585
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5370 - loss: 1.0547
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5376 - loss: 1.0552
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5379 - loss: 1.0556
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5375 - loss: 1.0562
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5367 - loss: 1.0571
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5363 - loss: 1.0578
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5363 - loss: 1.0581 - val_accuracy: 0.5762 - val_loss: 1.0637
Epoch 39/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 1.1547
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5355 - loss: 1.0728 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5369 - loss: 1.0651
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5356 - loss: 1.0631
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5360 - loss: 1.0620
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5351 - loss: 1.0627
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5347 - loss: 1.0628
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5341 - loss: 1.0630
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5339 - loss: 1.0630
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5338 - loss: 1.0628 - val_accuracy: 0.5713 - val_loss: 1.0750
Epoch 40/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 0.9836
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5283 - loss: 1.0451 
[1m 83/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5349 - loss: 1.0392
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5362 - loss: 1.0387
[1m163/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5363 - loss: 1.0413
[1m203/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5365 - loss: 1.0430
[1m244/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5364 - loss: 1.0450
[1m284/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5363 - loss: 1.0464
[1m323/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5361 - loss: 1.0477
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5361 - loss: 1.0479 - val_accuracy: 0.5593 - val_loss: 1.0956
Epoch 41/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.5312 - loss: 1.1338
[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5302 - loss: 1.0412 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5294 - loss: 1.0431
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5303 - loss: 1.0463
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5301 - loss: 1.0490
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5295 - loss: 1.0520
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5292 - loss: 1.0536
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5296 - loss: 1.0540
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5301 - loss: 1.0542
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5305 - loss: 1.0543 - val_accuracy: 0.5850 - val_loss: 1.0836
Epoch 42/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.4082
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5157 - loss: 1.1002 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5243 - loss: 1.0852
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5298 - loss: 1.0784
[1m162/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5326 - loss: 1.0754
[1m199/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5341 - loss: 1.0740
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5347 - loss: 1.0727
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5353 - loss: 1.0715
[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5363 - loss: 1.0698
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5365 - loss: 1.0694 - val_accuracy: 0.5734 - val_loss: 1.0782
Epoch 43/53

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Saved model to disk.
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[36m(train_cnn_ray_tune pid=2936972)[0m Epoch 56/75

=== EJECUCIÓN 1 ===

--- TRAIN (ejecución 1) ---

--- TEST (ejecución 1) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:30[0m 1s/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m68/89[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 752us/step
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Global accuracy score (validation) = 58.29 [%]
Global F1 score (validation) = 57.46 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.31311265 0.30028993 0.20745222 0.17914525]
 [0.3204863  0.21530096 0.433652   0.03056069]
 [0.31489378 0.2213966  0.43412766 0.02958194]
 ...
 [0.24951647 0.22680178 0.4210914  0.10259039]
 [0.25319514 0.18734688 0.43876705 0.12069103]
 [0.27879903 0.15759423 0.47802123 0.08558551]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 59.0 [%]
Global accuracy score (test) = 53.14 [%]
Global F1 score (train) = 59.18 [%]
Global F1 score (test) = 53.13 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.42      0.33      0.37       400
MODERATE-INTENSITY       0.48      0.59      0.53       400
         SEDENTARY       0.53      0.69      0.60       400
VIGOROUS-INTENSITY       0.79      0.51      0.62       345

          accuracy                           0.53      1545
         macro avg       0.56      0.53      0.53      1545
      weighted avg       0.55      0.53      0.53      1545

2025-11-05 10:38:59.819214: 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`.
2025-11-05 10:38:59.830469: 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 00:00:1762335539.843602 2986664 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762335539.847580 2986664 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762335539.857573 2986664 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335539.857592 2986664 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335539.857594 2986664 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335539.857596 2986664 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:38:59.860871: 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 00:00:1762335542.111421 2986664 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 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..
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/53
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762335543.748725 2986795 service.cc:152] XLA service 0x7526f8002dd0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762335543.748757 2986795 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:39:03.784118: 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 00:00:1762335543.959898 2986795 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762335546.103525 2986795 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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[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2955 - loss: 2.0256
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2971 - loss: 1.9819
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Epoch 2/53

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[1m161/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3349 - loss: 1.3748
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3370 - loss: 1.3722
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3389 - loss: 1.3699
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3409 - loss: 1.3675
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3425 - loss: 1.3653
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Epoch 3/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3582 - loss: 1.3200 
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[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3593 - loss: 1.3144
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3605 - loss: 1.3146
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3620 - loss: 1.3142
[1m226/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3640 - loss: 1.3134
[1m262/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3659 - loss: 1.3124
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3675 - loss: 1.3115
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3683 - loss: 1.3110 - val_accuracy: 0.4614 - val_loss: 1.2288
Epoch 4/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3952 - loss: 1.2584 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3976 - loss: 1.2661
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3986 - loss: 1.2696
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3994 - loss: 1.2713
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3997 - loss: 1.2731
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4006 - loss: 1.2740
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4010 - loss: 1.2749
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4009 - loss: 1.2757
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Epoch 5/53

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[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4268 - loss: 1.2451
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4199 - loss: 1.2526
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4165 - loss: 1.2555
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4145 - loss: 1.2566
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Epoch 6/53

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[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4123 - loss: 1.2462
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[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4119 - loss: 1.2478
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4121 - loss: 1.2480
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4123 - loss: 1.2480 - val_accuracy: 0.4902 - val_loss: 1.1846
Epoch 7/53

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[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4314 - loss: 1.2145 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4245 - loss: 1.2199
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[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4266 - loss: 1.2234
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4280 - loss: 1.2232
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4286 - loss: 1.2231
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4289 - loss: 1.2231
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4291 - loss: 1.2234
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4291 - loss: 1.2237 - val_accuracy: 0.4874 - val_loss: 1.1648
Epoch 8/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4380 - loss: 1.1897 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4306 - loss: 1.2020
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4301 - loss: 1.2054
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4305 - loss: 1.2077
[1m199/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4309 - loss: 1.2095
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4309 - loss: 1.2106
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4310 - loss: 1.2112
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4315 - loss: 1.2112
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4318 - loss: 1.2111 - val_accuracy: 0.5109 - val_loss: 1.1635
Epoch 9/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3750 - loss: 1.2963
[1m 42/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4486 - loss: 1.2170 
[1m 83/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4529 - loss: 1.2082
[1m123/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4531 - loss: 1.2046
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4522 - loss: 1.2048
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4518 - loss: 1.2044
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4517 - loss: 1.2039
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4510 - loss: 1.2040
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4502 - loss: 1.2043
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4498 - loss: 1.2044 - val_accuracy: 0.5004 - val_loss: 1.1581
Epoch 10/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4519 - loss: 1.1894 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4509 - loss: 1.1852
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4485 - loss: 1.1868
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4471 - loss: 1.1891
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4464 - loss: 1.1905
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4463 - loss: 1.1909
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4464 - loss: 1.1909
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4468 - loss: 1.1910
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4470 - loss: 1.1911 - val_accuracy: 0.4902 - val_loss: 1.1585
Epoch 11/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.3509
[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4466 - loss: 1.2243 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4465 - loss: 1.2108
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4485 - loss: 1.2040
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4486 - loss: 1.2000
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4489 - loss: 1.1966
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4497 - loss: 1.1939
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4509 - loss: 1.1920
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4516 - loss: 1.1911
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4518 - loss: 1.1909 - val_accuracy: 0.5263 - val_loss: 1.1534
Epoch 12/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4375 - loss: 1.2871
[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4534 - loss: 1.2216 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4559 - loss: 1.2086
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4559 - loss: 1.2019
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4570 - loss: 1.1962
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4585 - loss: 1.1918
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4596 - loss: 1.1887
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4601 - loss: 1.1868
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4607 - loss: 1.1850
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4610 - loss: 1.1843 - val_accuracy: 0.5119 - val_loss: 1.1373
Epoch 13/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4062 - loss: 1.2344
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4359 - loss: 1.1687 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4354 - loss: 1.1701
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4390 - loss: 1.1674
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4412 - loss: 1.1665
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4437 - loss: 1.1664
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4454 - loss: 1.1673
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4473 - loss: 1.1678
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4490 - loss: 1.1681
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4495 - loss: 1.1682 - val_accuracy: 0.5204 - val_loss: 1.1279
Epoch 14/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0209
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4728 - loss: 1.1432 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4716 - loss: 1.1496
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4707 - loss: 1.1531
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4686 - loss: 1.1571
[1m200/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4678 - loss: 1.1584
[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4674 - loss: 1.1587
[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4675 - loss: 1.1584
[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4674 - loss: 1.1587
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4673 - loss: 1.1589 - val_accuracy: 0.5341 - val_loss: 1.1422
Epoch 15/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4278 - loss: 1.2170 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4393 - loss: 1.2088
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4453 - loss: 1.2041
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4499 - loss: 1.1976
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[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4559 - loss: 1.1865
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4575 - loss: 1.1843
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4581 - loss: 1.1832 - val_accuracy: 0.5330 - val_loss: 1.1256
Epoch 16/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4642 - loss: 1.1498 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4709 - loss: 1.1463
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4741 - loss: 1.1426
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4749 - loss: 1.1423
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[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4756 - loss: 1.1425
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4754 - loss: 1.1436
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4749 - loss: 1.1447
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4748 - loss: 1.1452 - val_accuracy: 0.5162 - val_loss: 1.1298
Epoch 17/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4820 - loss: 1.1457 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4846 - loss: 1.1452
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4845 - loss: 1.1439
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4830 - loss: 1.1441
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[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4800 - loss: 1.1466
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4792 - loss: 1.1468
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4784 - loss: 1.1473
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4780 - loss: 1.1475 - val_accuracy: 0.5309 - val_loss: 1.1200
Epoch 18/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4585 - loss: 1.1391 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4595 - loss: 1.1419
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4633 - loss: 1.1408
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4649 - loss: 1.1410
[1m200/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4657 - loss: 1.1419
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4658 - loss: 1.1422
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4663 - loss: 1.1421
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4667 - loss: 1.1422
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4669 - loss: 1.1423 - val_accuracy: 0.5288 - val_loss: 1.1227
Epoch 19/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 25ms/step - accuracy: 0.5938 - loss: 1.0206
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4480 - loss: 1.1457 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4561 - loss: 1.1428
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4579 - loss: 1.1425
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4608 - loss: 1.1414
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4645 - loss: 1.1405
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4672 - loss: 1.1395
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4687 - loss: 1.1391
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4699 - loss: 1.1390
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4703 - loss: 1.1389 - val_accuracy: 0.5351 - val_loss: 1.1305
Epoch 20/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4275 - loss: 1.2028 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4469 - loss: 1.1870
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4565 - loss: 1.1744
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4610 - loss: 1.1668
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4644 - loss: 1.1615
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4674 - loss: 1.1573
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4699 - loss: 1.1540
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4718 - loss: 1.1516
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4724 - loss: 1.1508 - val_accuracy: 0.5337 - val_loss: 1.1220
Epoch 21/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.0244
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4717 - loss: 1.1441 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4741 - loss: 1.1472
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4758 - loss: 1.1445
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4762 - loss: 1.1427
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4770 - loss: 1.1420
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4777 - loss: 1.1411
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4787 - loss: 1.1401
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4795 - loss: 1.1395
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4798 - loss: 1.1393 - val_accuracy: 0.5365 - val_loss: 1.1119
Epoch 22/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6562 - loss: 1.0296
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4884 - loss: 1.1436 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4905 - loss: 1.1361
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4921 - loss: 1.1351
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4914 - loss: 1.1347
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4914 - loss: 1.1334
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4915 - loss: 1.1325
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4916 - loss: 1.1316
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4915 - loss: 1.1310
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4915 - loss: 1.1310 - val_accuracy: 0.5569 - val_loss: 1.1073
Epoch 23/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 1.1474
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5223 - loss: 1.1232 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5136 - loss: 1.1246
[1m110/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5071 - loss: 1.1266
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5035 - loss: 1.1277
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5020 - loss: 1.1279
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5013 - loss: 1.1274
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5011 - loss: 1.1266
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5011 - loss: 1.1257
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5011 - loss: 1.1252 - val_accuracy: 0.5393 - val_loss: 1.1044
Epoch 24/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4062 - loss: 1.2041
[1m 42/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4840 - loss: 1.1390 
[1m 84/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4931 - loss: 1.1247
[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4957 - loss: 1.1214
[1m166/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4962 - loss: 1.1215
[1m202/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4966 - loss: 1.1207
[1m244/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4973 - loss: 1.1198
[1m283/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4981 - loss: 1.1190
[1m323/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4988 - loss: 1.1183
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4989 - loss: 1.1182 - val_accuracy: 0.5474 - val_loss: 1.1127
Epoch 25/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4701 - loss: 1.1179 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4745 - loss: 1.1221
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4797 - loss: 1.1199
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4831 - loss: 1.1189
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4851 - loss: 1.1177
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4874 - loss: 1.1160
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4895 - loss: 1.1143
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4908 - loss: 1.1132
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4913 - loss: 1.1129 - val_accuracy: 0.5551 - val_loss: 1.1092
Epoch 26/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1079
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4677 - loss: 1.1192 
[1m 71/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4781 - loss: 1.1222
[1m107/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4824 - loss: 1.1217
[1m143/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4842 - loss: 1.1216
[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4852 - loss: 1.1217
[1m221/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4858 - loss: 1.1219
[1m261/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4872 - loss: 1.1208
[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4883 - loss: 1.1201
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4893 - loss: 1.1192 - val_accuracy: 0.5544 - val_loss: 1.1029
Epoch 27/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5011 - loss: 1.0992 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5007 - loss: 1.1017
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5023 - loss: 1.1052
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5031 - loss: 1.1065
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5034 - loss: 1.1070
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5043 - loss: 1.1060
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5053 - loss: 1.1050
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5060 - loss: 1.1045
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5061 - loss: 1.1045 - val_accuracy: 0.5460 - val_loss: 1.1057
Epoch 28/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 1.0220
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5092 - loss: 1.1229 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5063 - loss: 1.1117
[1m107/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5047 - loss: 1.1084
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5028 - loss: 1.1081
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5025 - loss: 1.1077
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5029 - loss: 1.1070
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5028 - loss: 1.1069
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5031 - loss: 1.1063
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5032 - loss: 1.1060 - val_accuracy: 0.5453 - val_loss: 1.1009
Epoch 29/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.6562 - loss: 1.1055
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5432 - loss: 1.0966 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5248 - loss: 1.0983
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5185 - loss: 1.1000
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5161 - loss: 1.1000
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5143 - loss: 1.1011
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5131 - loss: 1.1020
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5126 - loss: 1.1016
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5122 - loss: 1.1012
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5121 - loss: 1.1012 - val_accuracy: 0.5583 - val_loss: 1.0965
Epoch 30/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5096 - loss: 1.0760 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5126 - loss: 1.0737
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5133 - loss: 1.0756
[1m161/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5123 - loss: 1.0795
[1m203/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5114 - loss: 1.0823
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[1m281/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5100 - loss: 1.0855
[1m320/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5096 - loss: 1.0866
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5096 - loss: 1.0868 - val_accuracy: 0.5755 - val_loss: 1.1028
Epoch 31/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5221 - loss: 1.0717 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5223 - loss: 1.0685
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[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5251 - loss: 1.0700
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[1m263/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5229 - loss: 1.0760
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Epoch 32/53

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[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5268 - loss: 1.0840 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5165 - loss: 1.0972
[1m108/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5146 - loss: 1.0975
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5156 - loss: 1.0956
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[1m219/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5160 - loss: 1.0941
[1m258/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5159 - loss: 1.0933
[1m296/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5159 - loss: 1.0925
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5160 - loss: 1.0917 - val_accuracy: 0.5685 - val_loss: 1.0864
Epoch 33/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5036 - loss: 1.0695 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5084 - loss: 1.0614
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5107 - loss: 1.0631
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5108 - loss: 1.0676
[1m199/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5107 - loss: 1.0716
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5108 - loss: 1.0738
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5105 - loss: 1.0756
[1m319/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5105 - loss: 1.0767
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5105 - loss: 1.0769 - val_accuracy: 0.5583 - val_loss: 1.1007
Epoch 34/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6562 - loss: 0.9001
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5444 - loss: 1.0644 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5347 - loss: 1.0749
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5324 - loss: 1.0787
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5299 - loss: 1.0817
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[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5274 - loss: 1.0828
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5268 - loss: 1.0825
[1m319/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5264 - loss: 1.0819
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5263 - loss: 1.0817 - val_accuracy: 0.5576 - val_loss: 1.0930
Epoch 35/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4883 - loss: 1.1126 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5025 - loss: 1.0996
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[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5170 - loss: 1.0841
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5179 - loss: 1.0832
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Epoch 36/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5448 - loss: 1.0861 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5275 - loss: 1.0905
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Epoch 37/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5538 - loss: 1.0483 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5404 - loss: 1.0643
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5368 - loss: 1.0678
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5364 - loss: 1.0682
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[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5349 - loss: 1.0695
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5339 - loss: 1.0701
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5333 - loss: 1.0703
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5332 - loss: 1.0702 - val_accuracy: 0.5685 - val_loss: 1.0825
Epoch 38/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5446 - loss: 1.0650 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5371 - loss: 1.0697
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5364 - loss: 1.0692
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5368 - loss: 1.0693
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[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5361 - loss: 1.0698
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5352 - loss: 1.0699
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5343 - loss: 1.0702
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5337 - loss: 1.0703 - val_accuracy: 0.5723 - val_loss: 1.0905
Epoch 39/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5589 - loss: 1.0342 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5467 - loss: 1.0470
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[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5384 - loss: 1.0600
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[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5345 - loss: 1.0644
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5337 - loss: 1.0652
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Epoch 40/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5187 - loss: 1.0407 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5183 - loss: 1.0434
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[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5231 - loss: 1.0472
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Epoch 41/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5479 - loss: 1.0467 
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[1m146/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5385 - loss: 1.0630
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[1m261/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5368 - loss: 1.0605
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Epoch 42/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5646 - loss: 0.9935 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5483 - loss: 1.0195
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[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5442 - loss: 1.0309
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[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5425 - loss: 1.0361
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5415 - loss: 1.0389
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5403 - loss: 1.0416
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5398 - loss: 1.0425 - val_accuracy: 0.5664 - val_loss: 1.0811
Epoch 43/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5635 - loss: 1.0469 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5562 - loss: 1.0464
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5543 - loss: 1.0444
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5527 - loss: 1.0426
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[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5486 - loss: 1.0452
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5476 - loss: 1.0458
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5470 - loss: 1.0462
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5466 - loss: 1.0464 - val_accuracy: 0.5495 - val_loss: 1.0895
Epoch 44/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5459 - loss: 1.0259 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5352 - loss: 1.0388
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[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5328 - loss: 1.0436
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[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5348 - loss: 1.0444
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5356 - loss: 1.0449
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Epoch 45/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5555 - loss: 1.0415 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5460 - loss: 1.0438
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5432 - loss: 1.0434
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5421 - loss: 1.0428
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[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5426 - loss: 1.0412
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Epoch 46/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5580 - loss: 1.0442 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5501 - loss: 1.0438
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[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5482 - loss: 1.0468
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Epoch 47/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4872 - loss: 1.0929 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5026 - loss: 1.0810
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[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5239 - loss: 1.0562
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5251 - loss: 1.0551
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5255 - loss: 1.0547 - val_accuracy: 0.5846 - val_loss: 1.0804
Epoch 48/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5317 - loss: 1.0451  
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5365 - loss: 1.0376
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[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5427 - loss: 1.0320
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[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5434 - loss: 1.0318
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5431 - loss: 1.0327
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5428 - loss: 1.0334
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Epoch 49/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5336 - loss: 1.0395 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5405 - loss: 1.0419
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[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5461 - loss: 1.0359
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Saved model to disk.

Accuracy capturado en la ejecución 1: 53.14 [%]
F1-score capturado en la ejecución 1: 53.13 [%]

=== EJECUCIÓN 2 ===

--- TRAIN (ejecución 2) ---

--- TEST (ejecución 2) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:23[0m 990ms/step
[1m 66/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 772us/step  
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[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 746us/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m66/89[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 777us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 881us/step
Global accuracy score (validation) = 57.69 [%]
Global F1 score (validation) = 56.28 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.0606378  0.10556197 0.8275495  0.00625072]
 [0.15028167 0.11643834 0.6689934  0.06428663]
 [0.19187704 0.19408117 0.5877558  0.02628603]
 ...
 [0.27662766 0.2146868  0.32898414 0.17970137]
 [0.21316001 0.1222636  0.5418199  0.12275652]
 [0.10687106 0.10002495 0.7656188  0.02748512]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 59.44 [%]
Global accuracy score (test) = 51.97 [%]
Global F1 score (train) = 59.12 [%]
Global F1 score (test) = 51.32 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.41      0.28      0.33       400
MODERATE-INTENSITY       0.48      0.55      0.51       400
         SEDENTARY       0.51      0.74      0.61       400
VIGOROUS-INTENSITY       0.75      0.50      0.60       345

          accuracy                           0.52      1545
         macro avg       0.54      0.52      0.51      1545
      weighted avg       0.53      0.52      0.51      1545

2025-11-05 10:39:48.524065: 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`.
2025-11-05 10:39:48.535578: 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 00:00:1762335588.548648 2992239 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762335588.552737 2992239 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762335588.562452 2992239 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335588.562473 2992239 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335588.562475 2992239 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335588.562477 2992239 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:39:48.565612: 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 00:00:1762335590.789839 2992239 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 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..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/53
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762335592.421796 2992354 service.cc:152] XLA service 0x7e7d7001e120 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762335592.421823 2992354 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:39:52.454573: 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 00:00:1762335592.620523 2992354 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762335594.782653 2992354 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18:14[0m 3s/step - accuracy: 0.1875 - loss: 3.9707
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2589 - loss: 2.6477  
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2662 - loss: 2.4909
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2706 - loss: 2.3742
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2754 - loss: 2.2768
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2796 - loss: 2.1986
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2825 - loss: 2.1348
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[1m319/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2868 - loss: 2.0342
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Epoch 2/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3308 - loss: 1.3851 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3301 - loss: 1.3882
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3302 - loss: 1.3878
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3311 - loss: 1.3861
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[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3360 - loss: 1.3774
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3371 - loss: 1.3753
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Epoch 3/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3424 - loss: 1.3378 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3420 - loss: 1.3379
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3440 - loss: 1.3357
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3470 - loss: 1.3332
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3506 - loss: 1.3310
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3537 - loss: 1.3287
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3562 - loss: 1.3263
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3578 - loss: 1.3244
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3587 - loss: 1.3234 - val_accuracy: 0.5014 - val_loss: 1.2253
Epoch 4/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4194 - loss: 1.2702 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4161 - loss: 1.2660
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4094 - loss: 1.2673
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4055 - loss: 1.2691
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4021 - loss: 1.2714
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4009 - loss: 1.2728
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4000 - loss: 1.2736
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3993 - loss: 1.2741
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3990 - loss: 1.2742 - val_accuracy: 0.4989 - val_loss: 1.1839
Epoch 5/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.2339
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4509 - loss: 1.2472 
[1m 82/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4381 - loss: 1.2514
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4323 - loss: 1.2528
[1m162/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4299 - loss: 1.2530
[1m203/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4285 - loss: 1.2536
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[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4262 - loss: 1.2545
[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4252 - loss: 1.2546
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4250 - loss: 1.2545 - val_accuracy: 0.5140 - val_loss: 1.1897
Epoch 6/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4612 - loss: 1.2315 
[1m 82/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4507 - loss: 1.2342
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4458 - loss: 1.2360
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4421 - loss: 1.2362
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4406 - loss: 1.2358
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4393 - loss: 1.2350
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4383 - loss: 1.2343
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4371 - loss: 1.2341
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4368 - loss: 1.2340 - val_accuracy: 0.5133 - val_loss: 1.1675
Epoch 7/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4396 - loss: 1.2329 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4317 - loss: 1.2361
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4316 - loss: 1.2340
[1m162/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4325 - loss: 1.2307
[1m200/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4335 - loss: 1.2281
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4345 - loss: 1.2262
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4353 - loss: 1.2247
[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4359 - loss: 1.2238
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4360 - loss: 1.2236 - val_accuracy: 0.5232 - val_loss: 1.1687
Epoch 8/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4434 - loss: 1.2041 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4395 - loss: 1.2107
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4375 - loss: 1.2146
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4377 - loss: 1.2150
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4381 - loss: 1.2141
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4380 - loss: 1.2139
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4381 - loss: 1.2138
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4380 - loss: 1.2136
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4380 - loss: 1.2134 - val_accuracy: 0.5214 - val_loss: 1.1634
Epoch 9/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4553 - loss: 1.2087 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4536 - loss: 1.2042
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4530 - loss: 1.2019
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4522 - loss: 1.2009
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4506 - loss: 1.2016
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4494 - loss: 1.2014
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4483 - loss: 1.2012
[1m317/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4476 - loss: 1.2011
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4474 - loss: 1.2010 - val_accuracy: 0.5418 - val_loss: 1.1456
Epoch 10/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.1144
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4330 - loss: 1.2039 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4304 - loss: 1.2054
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4320 - loss: 1.2049
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4338 - loss: 1.2032
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4352 - loss: 1.2022
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4359 - loss: 1.2017
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4369 - loss: 1.2009
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4378 - loss: 1.2000
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4383 - loss: 1.1996 - val_accuracy: 0.5267 - val_loss: 1.1507
Epoch 11/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4267 - loss: 1.1849 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4358 - loss: 1.1896
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4408 - loss: 1.1912
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4438 - loss: 1.1913
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4457 - loss: 1.1906
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4469 - loss: 1.1904
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4480 - loss: 1.1898
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4487 - loss: 1.1895
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4492 - loss: 1.1891 - val_accuracy: 0.5277 - val_loss: 1.1319
Epoch 12/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3438 - loss: 1.3155
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4495 - loss: 1.2351 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4568 - loss: 1.2170
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4564 - loss: 1.2096
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4556 - loss: 1.2049
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4552 - loss: 1.2026
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4549 - loss: 1.2008
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4546 - loss: 1.1993
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4546 - loss: 1.1973
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4547 - loss: 1.1962 - val_accuracy: 0.5425 - val_loss: 1.1342
Epoch 13/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4732 - loss: 1.1505 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4755 - loss: 1.1549
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4760 - loss: 1.1567
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4757 - loss: 1.1578
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4747 - loss: 1.1591
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4738 - loss: 1.1599
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4733 - loss: 1.1602
[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4730 - loss: 1.1607
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4728 - loss: 1.1609 - val_accuracy: 0.5260 - val_loss: 1.1374
Epoch 14/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4062 - loss: 1.2399
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4380 - loss: 1.1954 
[1m 82/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4456 - loss: 1.1861
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4493 - loss: 1.1812
[1m161/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4537 - loss: 1.1765
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4561 - loss: 1.1745
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4582 - loss: 1.1733
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4601 - loss: 1.1725
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4615 - loss: 1.1719
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4621 - loss: 1.1717 - val_accuracy: 0.5372 - val_loss: 1.1290
Epoch 15/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.3750 - loss: 1.1787
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4855 - loss: 1.1751 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4861 - loss: 1.1657
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4833 - loss: 1.1642
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4818 - loss: 1.1623
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4809 - loss: 1.1612
[1m226/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4799 - loss: 1.1610
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4786 - loss: 1.1610
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4779 - loss: 1.1605
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4777 - loss: 1.1602 - val_accuracy: 0.5351 - val_loss: 1.1127
Epoch 16/53

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[1m 42/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4551 - loss: 1.1487 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4551 - loss: 1.1539
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[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4580 - loss: 1.1553
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[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4594 - loss: 1.1553
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4596 - loss: 1.1557
[1m317/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4599 - loss: 1.1557
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4600 - loss: 1.1557 - val_accuracy: 0.5471 - val_loss: 1.1264
Epoch 17/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4935 - loss: 1.1306 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4921 - loss: 1.1345
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4908 - loss: 1.1382
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4897 - loss: 1.1404
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4891 - loss: 1.1409
[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4889 - loss: 1.1405
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4883 - loss: 1.1407
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Epoch 18/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4828 - loss: 1.1462 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4827 - loss: 1.1415
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[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4803 - loss: 1.1420
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[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4806 - loss: 1.1433
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4808 - loss: 1.1434
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4812 - loss: 1.1431
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4814 - loss: 1.1431 - val_accuracy: 0.5460 - val_loss: 1.1180
Epoch 19/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4980 - loss: 1.0991 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4846 - loss: 1.1200
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4813 - loss: 1.1264
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4804 - loss: 1.1288
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4791 - loss: 1.1318
[1m226/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4778 - loss: 1.1346
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4771 - loss: 1.1361
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4768 - loss: 1.1369
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4766 - loss: 1.1375 - val_accuracy: 0.5488 - val_loss: 1.1236
Epoch 20/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5074 - loss: 1.1020 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5016 - loss: 1.1175
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4953 - loss: 1.1263
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4928 - loss: 1.1279
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[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4895 - loss: 1.1299
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Saved model to disk.

Accuracy capturado en la ejecución 2: 51.97 [%]
F1-score capturado en la ejecución 2: 51.32 [%]

=== EJECUCIÓN 3 ===

--- TRAIN (ejecución 3) ---

--- TEST (ejecución 3) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:21[0m 984ms/step
[1m 69/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 741us/step  
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[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 751us/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m66/89[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 773us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 881us/step
Global accuracy score (validation) = 54.46 [%]
Global F1 score (validation) = 52.22 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.21180966 0.21864174 0.521212   0.04833664]
 [0.34479606 0.5229717  0.10212962 0.03010262]
 [0.21455309 0.18073693 0.5570249  0.04768509]
 ...
 [0.23369741 0.16494955 0.4778598  0.12349325]
 [0.22435744 0.13574317 0.4557438  0.18415563]
 [0.22332115 0.17998016 0.5499509  0.04674774]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 52.36 [%]
Global accuracy score (test) = 50.49 [%]
Global F1 score (train) = 51.43 [%]
Global F1 score (test) = 49.64 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.44      0.22      0.29       400
MODERATE-INTENSITY       0.43      0.68      0.53       400
         SEDENTARY       0.51      0.61      0.55       400
VIGOROUS-INTENSITY       0.76      0.51      0.61       345

          accuracy                           0.50      1545
         macro avg       0.53      0.51      0.50      1545
      weighted avg       0.53      0.50      0.49      1545

2025-11-05 10:40:20.013586: 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`.
2025-11-05 10:40:20.025329: 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 00:00:1762335620.038974 2995051 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762335620.043264 2995051 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762335620.053209 2995051 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335620.053228 2995051 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335620.053231 2995051 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335620.053233 2995051 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:40:20.056514: 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 00:00:1762335622.304207 2995051 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 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..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/53
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762335623.910332 2995162 service.cc:152] XLA service 0x706f3000b800 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762335623.910364 2995162 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:40:23.944240: 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 00:00:1762335624.111149 2995162 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762335626.273103 2995162 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18:16[0m 3s/step - accuracy: 0.1875 - loss: 2.7506
[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2564 - loss: 2.6436  
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2663 - loss: 2.4721
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2731 - loss: 2.3442
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2793 - loss: 2.2456
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2834 - loss: 2.1663
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[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2890 - loss: 2.0513
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2911 - loss: 2.0054
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Epoch 2/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3477 - loss: 1.3641 
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[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3447 - loss: 1.3724
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[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3443 - loss: 1.3687
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3443 - loss: 1.3676
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Epoch 3/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3404 - loss: 1.3306 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3436 - loss: 1.3331
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[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3493 - loss: 1.3322
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3508 - loss: 1.3311
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3522 - loss: 1.3301
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3537 - loss: 1.3290
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3552 - loss: 1.3278
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3559 - loss: 1.3272 - val_accuracy: 0.4779 - val_loss: 1.2262
Epoch 4/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4062 - loss: 1.2782
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3834 - loss: 1.3019 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3863 - loss: 1.2961
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3874 - loss: 1.2923
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3890 - loss: 1.2901
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3899 - loss: 1.2888
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3903 - loss: 1.2881
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3907 - loss: 1.2874
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3914 - loss: 1.2866
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3920 - loss: 1.2860 - val_accuracy: 0.4874 - val_loss: 1.1963
Epoch 5/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4190 - loss: 1.2546 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4199 - loss: 1.2528
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[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4168 - loss: 1.2538
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[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4168 - loss: 1.2554
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4167 - loss: 1.2559
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4164 - loss: 1.2560
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4163 - loss: 1.2560 - val_accuracy: 0.4993 - val_loss: 1.1901
Epoch 6/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4206 - loss: 1.2688 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4159 - loss: 1.2636
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4142 - loss: 1.2604
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4149 - loss: 1.2578
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4156 - loss: 1.2566
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4162 - loss: 1.2555
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4164 - loss: 1.2547
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4167 - loss: 1.2536
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4169 - loss: 1.2531 - val_accuracy: 0.5070 - val_loss: 1.1707
Epoch 7/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 19ms/step - accuracy: 0.4375 - loss: 1.2028
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4365 - loss: 1.2309 
[1m 71/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4357 - loss: 1.2275
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4354 - loss: 1.2250
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4354 - loss: 1.2241
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4368 - loss: 1.2235
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4378 - loss: 1.2235
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4381 - loss: 1.2234
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4381 - loss: 1.2232
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4382 - loss: 1.2232 - val_accuracy: 0.5200 - val_loss: 1.1592
Epoch 8/53

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[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4203 - loss: 1.2343 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4331 - loss: 1.2239
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4395 - loss: 1.2187
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4433 - loss: 1.2156
[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4439 - loss: 1.2143
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4444 - loss: 1.2131
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4441 - loss: 1.2129
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4440 - loss: 1.2126
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4440 - loss: 1.2124 - val_accuracy: 0.5176 - val_loss: 1.1439
Epoch 9/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.2584
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4426 - loss: 1.2167 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4399 - loss: 1.2167
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4406 - loss: 1.2141
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4410 - loss: 1.2121
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4419 - loss: 1.2104
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4434 - loss: 1.2088
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4447 - loss: 1.2075
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4457 - loss: 1.2063
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4460 - loss: 1.2059 - val_accuracy: 0.5046 - val_loss: 1.1508
Epoch 10/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1501
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4321 - loss: 1.2228 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4414 - loss: 1.2137
[1m110/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4442 - loss: 1.2104
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4473 - loss: 1.2058
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4502 - loss: 1.2022
[1m226/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4518 - loss: 1.2000
[1m262/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4528 - loss: 1.1988
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4535 - loss: 1.1977
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4540 - loss: 1.1970 - val_accuracy: 0.5179 - val_loss: 1.1433
Epoch 11/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4080 - loss: 1.2384 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4173 - loss: 1.2251
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4218 - loss: 1.2171
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4254 - loss: 1.2118
[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4287 - loss: 1.2082
[1m224/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4314 - loss: 1.2059
[1m260/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4338 - loss: 1.2036
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4364 - loss: 1.2011
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4377 - loss: 1.1998 - val_accuracy: 0.5165 - val_loss: 1.1413
Epoch 12/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3438 - loss: 1.2897
[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4213 - loss: 1.1992 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4364 - loss: 1.1895
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4407 - loss: 1.1877
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4443 - loss: 1.1858
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4471 - loss: 1.1846
[1m224/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4489 - loss: 1.1835
[1m263/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4504 - loss: 1.1826
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4513 - loss: 1.1820
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4518 - loss: 1.1815 - val_accuracy: 0.5232 - val_loss: 1.1233
Epoch 13/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4797 - loss: 1.1735 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4705 - loss: 1.1837
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4688 - loss: 1.1839
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4682 - loss: 1.1827
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4684 - loss: 1.1817
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4688 - loss: 1.1806
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4692 - loss: 1.1792
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4696 - loss: 1.1778
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4697 - loss: 1.1774 - val_accuracy: 0.5337 - val_loss: 1.1144
Epoch 14/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.2332
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4551 - loss: 1.1766 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4577 - loss: 1.1691
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4597 - loss: 1.1668
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4591 - loss: 1.1668
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4597 - loss: 1.1669
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4610 - loss: 1.1667
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4625 - loss: 1.1663
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4641 - loss: 1.1657
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4648 - loss: 1.1654 - val_accuracy: 0.5260 - val_loss: 1.1257
Epoch 15/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3750 - loss: 1.0953
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4713 - loss: 1.1477 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4699 - loss: 1.1466
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4689 - loss: 1.1489
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4685 - loss: 1.1504
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4686 - loss: 1.1505
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4691 - loss: 1.1504
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4695 - loss: 1.1507
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4698 - loss: 1.1512
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4699 - loss: 1.1515 - val_accuracy: 0.5295 - val_loss: 1.1359
Epoch 16/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4284 - loss: 1.1699 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4467 - loss: 1.1568
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4544 - loss: 1.1521
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4595 - loss: 1.1494
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4629 - loss: 1.1482
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4654 - loss: 1.1474
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4671 - loss: 1.1472
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4684 - loss: 1.1470
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4687 - loss: 1.1473 - val_accuracy: 0.5499 - val_loss: 1.1110
Epoch 17/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 1.1720
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4565 - loss: 1.2043 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4583 - loss: 1.1937
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4626 - loss: 1.1841
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4665 - loss: 1.1774
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4696 - loss: 1.1722
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4721 - loss: 1.1681
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4737 - loss: 1.1651
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4744 - loss: 1.1634
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4747 - loss: 1.1625 - val_accuracy: 0.5355 - val_loss: 1.1216
Epoch 18/53

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[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4874 - loss: 1.1260 
[1m 71/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4868 - loss: 1.1355
[1m110/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4840 - loss: 1.1390
[1m145/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4825 - loss: 1.1409
[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4819 - loss: 1.1418
[1m223/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4813 - loss: 1.1424
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4807 - loss: 1.1434
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4804 - loss: 1.1441
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4804 - loss: 1.1443 - val_accuracy: 0.5393 - val_loss: 1.1128
Epoch 19/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 19ms/step - accuracy: 0.4688 - loss: 1.0601
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4930 - loss: 1.1181 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4925 - loss: 1.1254
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4914 - loss: 1.1285
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4895 - loss: 1.1318
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4889 - loss: 1.1330
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4893 - loss: 1.1327
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4901 - loss: 1.1318
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4908 - loss: 1.1309
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4911 - loss: 1.1306 - val_accuracy: 0.5327 - val_loss: 1.1074
Epoch 20/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4062 - loss: 1.1090
[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4556 - loss: 1.1673 
[1m 71/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4638 - loss: 1.1579
[1m110/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4669 - loss: 1.1532
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4673 - loss: 1.1517
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4678 - loss: 1.1506
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4686 - loss: 1.1494
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4695 - loss: 1.1482
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4706 - loss: 1.1471
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4712 - loss: 1.1466 - val_accuracy: 0.5337 - val_loss: 1.1078
Epoch 21/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5184 - loss: 1.1208 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5137 - loss: 1.1158
[1m105/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5109 - loss: 1.1137
[1m136/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5089 - loss: 1.1125
[1m175/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5070 - loss: 1.1115
[1m210/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5054 - loss: 1.1113
[1m247/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5038 - loss: 1.1117
[1m283/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5025 - loss: 1.1123
[1m324/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5013 - loss: 1.1131
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5011 - loss: 1.1132 - val_accuracy: 0.5393 - val_loss: 1.1105
Epoch 22/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.2796
[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4845 - loss: 1.1706 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4917 - loss: 1.1502
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4940 - loss: 1.1425
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4950 - loss: 1.1366
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4952 - loss: 1.1333
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4951 - loss: 1.1311
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4951 - loss: 1.1296
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4953 - loss: 1.1281
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4954 - loss: 1.1274 - val_accuracy: 0.5478 - val_loss: 1.1116
Epoch 23/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3438 - loss: 1.2626
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4953 - loss: 1.1059 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4980 - loss: 1.1062
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5002 - loss: 1.1080
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5007 - loss: 1.1093
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5001 - loss: 1.1100
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4998 - loss: 1.1103
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4997 - loss: 1.1106
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4996 - loss: 1.1111
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4994 - loss: 1.1115 - val_accuracy: 0.5432 - val_loss: 1.1206
Epoch 24/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 28ms/step - accuracy: 0.5625 - loss: 1.2391
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5322 - loss: 1.1135 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5213 - loss: 1.1112
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5138 - loss: 1.1150
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5102 - loss: 1.1161
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5083 - loss: 1.1165
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5076 - loss: 1.1159
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5071 - loss: 1.1149
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5064 - loss: 1.1143
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5061 - loss: 1.1142 - val_accuracy: 0.5474 - val_loss: 1.1000
Epoch 25/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 1.0666
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4930 - loss: 1.1332 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4944 - loss: 1.1288
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4951 - loss: 1.1264
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4947 - loss: 1.1260
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4944 - loss: 1.1251
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4945 - loss: 1.1238
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4949 - loss: 1.1226
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4951 - loss: 1.1211
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4954 - loss: 1.1204 - val_accuracy: 0.5478 - val_loss: 1.0977
Epoch 26/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1081
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5047 - loss: 1.0943 
[1m 69/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5105 - loss: 1.0942
[1m108/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5140 - loss: 1.0958
[1m142/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5145 - loss: 1.0960
[1m179/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5132 - loss: 1.0978
[1m217/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5120 - loss: 1.0995
[1m257/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5110 - loss: 1.1002
[1m294/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5100 - loss: 1.1010
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5093 - loss: 1.1016 - val_accuracy: 0.5562 - val_loss: 1.0973
Epoch 27/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1453
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5254 - loss: 1.0755 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5243 - loss: 1.0777
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5181 - loss: 1.0843
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5137 - loss: 1.0896
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5111 - loss: 1.0930
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5102 - loss: 1.0942
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5099 - loss: 1.0947
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5096 - loss: 1.0952
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5094 - loss: 1.0953 - val_accuracy: 0.5579 - val_loss: 1.0939
Epoch 28/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5312 - loss: 1.0263
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5114 - loss: 1.1099 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5136 - loss: 1.1009
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5166 - loss: 1.0933
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5171 - loss: 1.0927
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5172 - loss: 1.0934
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5171 - loss: 1.0944
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5167 - loss: 1.0951
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5165 - loss: 1.0952
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5164 - loss: 1.0953 - val_accuracy: 0.5639 - val_loss: 1.0944
Epoch 29/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6562 - loss: 1.0048
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5161 - loss: 1.0858 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5164 - loss: 1.0868
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5160 - loss: 1.0880
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5158 - loss: 1.0908
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5156 - loss: 1.0931
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5156 - loss: 1.0943
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5155 - loss: 1.0950
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5150 - loss: 1.0957
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5147 - loss: 1.0958 - val_accuracy: 0.5411 - val_loss: 1.1007
Epoch 30/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4375 - loss: 1.1052
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5164 - loss: 1.0774 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5226 - loss: 1.0840
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5211 - loss: 1.0876
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5195 - loss: 1.0897
[1m199/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5182 - loss: 1.0904
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5174 - loss: 1.0908
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5167 - loss: 1.0916
[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5157 - loss: 1.0923
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5155 - loss: 1.0924 - val_accuracy: 0.5583 - val_loss: 1.1051
Epoch 31/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.2859
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4844 - loss: 1.1240 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4942 - loss: 1.1111
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4979 - loss: 1.1055
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5011 - loss: 1.1023
[1m200/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5041 - loss: 1.0995
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5061 - loss: 1.0978
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5076 - loss: 1.0965
[1m317/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5085 - loss: 1.0954
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5088 - loss: 1.0950 - val_accuracy: 0.5772 - val_loss: 1.0751
Epoch 32/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4375 - loss: 1.1170
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5154 - loss: 1.0809 
[1m 82/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5202 - loss: 1.0700
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5216 - loss: 1.0664
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5228 - loss: 1.0651
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5230 - loss: 1.0665
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5228 - loss: 1.0682
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5223 - loss: 1.0696
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5216 - loss: 1.0706
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5212 - loss: 1.0711 - val_accuracy: 0.5593 - val_loss: 1.0895
Epoch 33/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 19ms/step - accuracy: 0.5625 - loss: 0.9996
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5261 - loss: 1.0583 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5247 - loss: 1.0676
[1m109/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5233 - loss: 1.0709
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5238 - loss: 1.0719
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5246 - loss: 1.0713
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5248 - loss: 1.0709
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5244 - loss: 1.0709
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5240 - loss: 1.0711
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5237 - loss: 1.0712 - val_accuracy: 0.5657 - val_loss: 1.0898
Epoch 34/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 1.0472
[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5413 - loss: 1.0473 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5369 - loss: 1.0571
[1m108/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5326 - loss: 1.0654
[1m145/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5295 - loss: 1.0694
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5279 - loss: 1.0711
[1m222/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5274 - loss: 1.0713
[1m260/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5268 - loss: 1.0717
[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5261 - loss: 1.0723
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5257 - loss: 1.0726 - val_accuracy: 0.5765 - val_loss: 1.0852
Epoch 35/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 1.0118
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5284 - loss: 1.0469 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5276 - loss: 1.0544
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5296 - loss: 1.0578
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5303 - loss: 1.0601
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5305 - loss: 1.0612
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5305 - loss: 1.0614
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5304 - loss: 1.0617
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5299 - loss: 1.0626
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Epoch 36/53

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 403ms/step
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Saved model to disk.

Accuracy capturado en la ejecución 3: 50.49 [%]
F1-score capturado en la ejecución 3: 49.64 [%]

=== EJECUCIÓN 4 ===

--- TRAIN (ejecución 4) ---

--- TEST (ejecución 4) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:49[0m 1s/step
[1m 64/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 802us/step
[1m131/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 775us/step
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 777us/step
[1m263/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 767us/step
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m61/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 838us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 929us/step
Global accuracy score (validation) = 58.29 [%]
Global F1 score (validation) = 57.4 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.11611869 0.11251988 0.75476444 0.01659704]
 [0.25520176 0.16487285 0.5321741  0.0477513 ]
 [0.10572082 0.10820445 0.7724675  0.01360719]
 ...
 [0.28463736 0.17169899 0.42941225 0.1142513 ]
 [0.22506842 0.09995021 0.33560342 0.339378  ]
 [0.24624327 0.13460171 0.53666645 0.08248858]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 57.3 [%]
Global accuracy score (test) = 50.36 [%]
Global F1 score (train) = 57.41 [%]
Global F1 score (test) = 50.33 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.35      0.30      0.33       400
MODERATE-INTENSITY       0.46      0.51      0.48       400
         SEDENTARY       0.52      0.71      0.60       400
VIGOROUS-INTENSITY       0.78      0.49      0.60       345

          accuracy                           0.50      1545
         macro avg       0.53      0.50      0.50      1545
      weighted avg       0.52      0.50      0.50      1545

2025-11-05 10:41:01.215530: 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`.
2025-11-05 10:41:01.226916: 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 00:00:1762335661.240050 2999372 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762335661.244225 2999372 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762335661.254070 2999372 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335661.254088 2999372 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335661.254091 2999372 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335661.254092 2999372 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:41:01.257397: 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 00:00:1762335663.490139 2999372 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 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..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/53
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762335665.112261 2999503 service.cc:152] XLA service 0x74320400c350 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762335665.112290 2999503 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:41:05.145423: 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 00:00:1762335665.320932 2999503 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762335667.509081 2999503 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 2/53

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[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3335 - loss: 1.3993
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3369 - loss: 1.3942
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3405 - loss: 1.3887
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3429 - loss: 1.3849
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3448 - loss: 1.3813
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Epoch 3/53

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[1m142/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3878 - loss: 1.3115
[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3906 - loss: 1.3093
[1m222/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3913 - loss: 1.3084
[1m258/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3916 - loss: 1.3077
[1m296/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3914 - loss: 1.3073
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Epoch 4/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3986 - loss: 1.2802 
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[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3977 - loss: 1.2859
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3970 - loss: 1.2860
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3964 - loss: 1.2859
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3962 - loss: 1.2857
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3967 - loss: 1.2852
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Epoch 5/53

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[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4047 - loss: 1.2533
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Epoch 6/53

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[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4318 - loss: 1.2340
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[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4279 - loss: 1.2346
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[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4292 - loss: 1.2326
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4289 - loss: 1.2326
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4287 - loss: 1.2327 - val_accuracy: 0.4898 - val_loss: 1.1606
Epoch 7/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4515 - loss: 1.2498 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4440 - loss: 1.2425
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4412 - loss: 1.2373
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4415 - loss: 1.2342
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4421 - loss: 1.2316
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4423 - loss: 1.2301
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4423 - loss: 1.2290
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4421 - loss: 1.2284
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4421 - loss: 1.2280 - val_accuracy: 0.5116 - val_loss: 1.1524
Epoch 8/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4296 - loss: 1.2349 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4378 - loss: 1.2256
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4415 - loss: 1.2232
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4437 - loss: 1.2207
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4445 - loss: 1.2199
[1m223/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4452 - loss: 1.2190
[1m263/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4458 - loss: 1.2178
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4463 - loss: 1.2166
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4465 - loss: 1.2160 - val_accuracy: 0.5165 - val_loss: 1.1548
Epoch 9/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4431 - loss: 1.2037 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4526 - loss: 1.1916
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4566 - loss: 1.1863
[1m161/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4590 - loss: 1.1844
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4591 - loss: 1.1843
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4585 - loss: 1.1849
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4576 - loss: 1.1862
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4567 - loss: 1.1875
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4563 - loss: 1.1882 - val_accuracy: 0.4996 - val_loss: 1.1459
Epoch 10/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4485 - loss: 1.1714 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4557 - loss: 1.1630
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4560 - loss: 1.1668
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4560 - loss: 1.1709
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4551 - loss: 1.1740
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4542 - loss: 1.1767
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4538 - loss: 1.1785
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4539 - loss: 1.1795
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4540 - loss: 1.1801 - val_accuracy: 0.5158 - val_loss: 1.1542
Epoch 11/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 1.0472
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4645 - loss: 1.1782 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4603 - loss: 1.1813
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4585 - loss: 1.1838
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4589 - loss: 1.1839
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4594 - loss: 1.1840
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4600 - loss: 1.1839
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4600 - loss: 1.1842
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4598 - loss: 1.1844
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4599 - loss: 1.1843 - val_accuracy: 0.5305 - val_loss: 1.1349
Epoch 12/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4794 - loss: 1.1266 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4749 - loss: 1.1457
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4685 - loss: 1.1585
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4648 - loss: 1.1643
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4630 - loss: 1.1677
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4617 - loss: 1.1698
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4609 - loss: 1.1709
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4610 - loss: 1.1713
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4612 - loss: 1.1714 - val_accuracy: 0.5147 - val_loss: 1.1342
Epoch 13/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.4688 - loss: 1.2652
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4731 - loss: 1.1609 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4662 - loss: 1.1654
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4640 - loss: 1.1676
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4629 - loss: 1.1683
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4617 - loss: 1.1691
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4610 - loss: 1.1696
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4610 - loss: 1.1696
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4608 - loss: 1.1697
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4608 - loss: 1.1697 - val_accuracy: 0.5288 - val_loss: 1.1391
Epoch 14/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3750 - loss: 1.2336
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4458 - loss: 1.2078 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4479 - loss: 1.1979
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4522 - loss: 1.1915
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4546 - loss: 1.1873
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4567 - loss: 1.1838
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4587 - loss: 1.1806
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4604 - loss: 1.1784
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4619 - loss: 1.1766
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4625 - loss: 1.1758 - val_accuracy: 0.5274 - val_loss: 1.1287
Epoch 15/53

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[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4677 - loss: 1.1719 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4664 - loss: 1.1706
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4673 - loss: 1.1685
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4678 - loss: 1.1664
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4688 - loss: 1.1652
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4695 - loss: 1.1643
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4704 - loss: 1.1630
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4713 - loss: 1.1618
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4719 - loss: 1.1613 - val_accuracy: 0.5246 - val_loss: 1.1191
Epoch 16/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1232
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4664 - loss: 1.1431 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4723 - loss: 1.1476
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4740 - loss: 1.1525
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4756 - loss: 1.1544
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4764 - loss: 1.1551
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4772 - loss: 1.1546
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4776 - loss: 1.1544
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4779 - loss: 1.1542
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4781 - loss: 1.1540 - val_accuracy: 0.5404 - val_loss: 1.1354
Epoch 17/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4886 - loss: 1.1423 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4862 - loss: 1.1423
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4842 - loss: 1.1432
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4842 - loss: 1.1434
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4841 - loss: 1.1429
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4838 - loss: 1.1425
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4832 - loss: 1.1425
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4825 - loss: 1.1425
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4821 - loss: 1.1427 - val_accuracy: 0.5327 - val_loss: 1.1322
Epoch 18/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 1.0122
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4794 - loss: 1.1132 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4815 - loss: 1.1169
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4824 - loss: 1.1197
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4819 - loss: 1.1231
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4816 - loss: 1.1254
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4810 - loss: 1.1268
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4804 - loss: 1.1282
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4804 - loss: 1.1291
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4806 - loss: 1.1296 - val_accuracy: 0.5513 - val_loss: 1.1143
Epoch 19/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.2241
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4929 - loss: 1.1309 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4919 - loss: 1.1282
[1m109/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4925 - loss: 1.1284
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4918 - loss: 1.1282
[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4917 - loss: 1.1278
[1m222/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4911 - loss: 1.1281
[1m262/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4903 - loss: 1.1285
[1m295/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4895 - loss: 1.1289
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4887 - loss: 1.1294 - val_accuracy: 0.5467 - val_loss: 1.1190
Epoch 20/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5264 - loss: 1.0802 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5088 - loss: 1.0998
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5033 - loss: 1.1061
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5006 - loss: 1.1097
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4981 - loss: 1.1139
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4961 - loss: 1.1173
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4947 - loss: 1.1192
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4938 - loss: 1.1208
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4934 - loss: 1.1217 - val_accuracy: 0.5586 - val_loss: 1.1096
Epoch 21/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4988 - loss: 1.1238 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4943 - loss: 1.1304
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4894 - loss: 1.1344
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4866 - loss: 1.1356
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[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4844 - loss: 1.1355
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4841 - loss: 1.1346
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4841 - loss: 1.1339
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4839 - loss: 1.1337 - val_accuracy: 0.5376 - val_loss: 1.1159
Epoch 22/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4994 - loss: 1.1076 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4908 - loss: 1.1233
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4911 - loss: 1.1271
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4912 - loss: 1.1292
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4908 - loss: 1.1301
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4909 - loss: 1.1296
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4908 - loss: 1.1293
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4909 - loss: 1.1284
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4910 - loss: 1.1280 - val_accuracy: 0.5530 - val_loss: 1.1085
Epoch 23/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5472 - loss: 1.0595 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5272 - loss: 1.0744
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5164 - loss: 1.0846
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5107 - loss: 1.0911
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5065 - loss: 1.0971
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5040 - loss: 1.1005
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5019 - loss: 1.1032
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5008 - loss: 1.1045
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5005 - loss: 1.1048 - val_accuracy: 0.5600 - val_loss: 1.0976
Epoch 24/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 0.9853
[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4646 - loss: 1.1572 
[1m 70/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4793 - loss: 1.1521
[1m109/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4882 - loss: 1.1418
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4928 - loss: 1.1353
[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4954 - loss: 1.1308
[1m223/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4973 - loss: 1.1275
[1m261/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4986 - loss: 1.1254
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4993 - loss: 1.1239
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4997 - loss: 1.1230 - val_accuracy: 0.5467 - val_loss: 1.1002
Epoch 25/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5062 - loss: 1.1010 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5087 - loss: 1.1062
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5081 - loss: 1.1063
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5075 - loss: 1.1061
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5070 - loss: 1.1055
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5063 - loss: 1.1051
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5060 - loss: 1.1046
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5058 - loss: 1.1046
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5055 - loss: 1.1047 - val_accuracy: 0.5702 - val_loss: 1.1002
Epoch 26/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4375 - loss: 1.0770
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5021 - loss: 1.1328 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5031 - loss: 1.1201
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5010 - loss: 1.1145
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5005 - loss: 1.1110
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5003 - loss: 1.1093
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5005 - loss: 1.1079
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5005 - loss: 1.1066
[1m319/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5009 - loss: 1.1053
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5010 - loss: 1.1051 - val_accuracy: 0.5506 - val_loss: 1.0994
Epoch 27/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.0455
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5028 - loss: 1.0864 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5045 - loss: 1.1008
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5049 - loss: 1.1041
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5062 - loss: 1.1047
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5074 - loss: 1.1044
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5082 - loss: 1.1036
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5085 - loss: 1.1030
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5085 - loss: 1.1027
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5085 - loss: 1.1026 - val_accuracy: 0.5758 - val_loss: 1.0871
Epoch 28/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3750 - loss: 1.1243
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4755 - loss: 1.1162 
[1m 82/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4859 - loss: 1.1135
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4905 - loss: 1.1106
[1m163/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4949 - loss: 1.1067
[1m200/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4979 - loss: 1.1037
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5002 - loss: 1.1016
[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5017 - loss: 1.1002
[1m322/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5025 - loss: 1.0996
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5026 - loss: 1.0996 - val_accuracy: 0.5597 - val_loss: 1.0922
Epoch 29/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4062 - loss: 1.0590
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4804 - loss: 1.1105 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4878 - loss: 1.1031
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4930 - loss: 1.0976
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4957 - loss: 1.0957
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4991 - loss: 1.0933
[1m226/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5008 - loss: 1.0934
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5020 - loss: 1.0937
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5024 - loss: 1.0939
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5027 - loss: 1.0937 - val_accuracy: 0.5751 - val_loss: 1.0945
Epoch 30/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4979 - loss: 1.1044 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5086 - loss: 1.0908
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5078 - loss: 1.0919
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5068 - loss: 1.0932
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[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5058 - loss: 1.0953
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Epoch 31/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5335 - loss: 1.0642 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5354 - loss: 1.0608
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[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5299 - loss: 1.0653
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[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5245 - loss: 1.0708
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5233 - loss: 1.0720
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Epoch 32/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5476 - loss: 1.0754 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5432 - loss: 1.0706
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[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5357 - loss: 1.0735
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[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5326 - loss: 1.0735
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5317 - loss: 1.0731
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5308 - loss: 1.0731
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5304 - loss: 1.0733 - val_accuracy: 0.5692 - val_loss: 1.1093
Epoch 33/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4835 - loss: 1.1098 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4893 - loss: 1.1020
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4958 - loss: 1.0934
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5000 - loss: 1.0885
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5026 - loss: 1.0850
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5046 - loss: 1.0825
[1m263/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5055 - loss: 1.0816
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5062 - loss: 1.0812
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5067 - loss: 1.0810 - val_accuracy: 0.5818 - val_loss: 1.0719
Epoch 34/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 1.0236
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5048 - loss: 1.0986 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5111 - loss: 1.0854
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5135 - loss: 1.0827
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5150 - loss: 1.0810
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[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5177 - loss: 1.0776
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5179 - loss: 1.0770
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5181 - loss: 1.0766 - val_accuracy: 0.5709 - val_loss: 1.0940
Epoch 35/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5420 - loss: 1.0850 
[1m 82/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5390 - loss: 1.0865
[1m123/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5348 - loss: 1.0863
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5315 - loss: 1.0860
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5293 - loss: 1.0850
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5280 - loss: 1.0839
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5270 - loss: 1.0833
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5262 - loss: 1.0829
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5258 - loss: 1.0827 - val_accuracy: 0.5720 - val_loss: 1.0803
Epoch 36/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.0787
[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5543 - loss: 1.0135 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5453 - loss: 1.0314
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5386 - loss: 1.0429
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5351 - loss: 1.0474
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[1m223/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5310 - loss: 1.0528
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5295 - loss: 1.0547
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5284 - loss: 1.0563
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5280 - loss: 1.0570 - val_accuracy: 0.5829 - val_loss: 1.0789
Epoch 37/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.2669
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5338 - loss: 1.0624 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5275 - loss: 1.0693
[1m109/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5254 - loss: 1.0696
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5252 - loss: 1.0671
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[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5258 - loss: 1.0633
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5256 - loss: 1.0624
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5252 - loss: 1.0626
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5250 - loss: 1.0627 - val_accuracy: 0.5667 - val_loss: 1.0956
Epoch 38/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6562 - loss: 0.8766
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5477 - loss: 1.0249 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5389 - loss: 1.0365
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 409ms/step
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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 16ms/step
Saved model to disk.

Accuracy capturado en la ejecución 4: 50.36 [%]
F1-score capturado en la ejecución 4: 50.33 [%]

=== EJECUCIÓN 5 ===

--- TRAIN (ejecución 5) ---

--- TEST (ejecución 5) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 15ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m65/89[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 788us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 935us/step
Global accuracy score (validation) = 58.01 [%]
Global F1 score (validation) = 56.1 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.12680838 0.12131971 0.7273801  0.0244919 ]
 [0.12318939 0.0691279  0.7605801  0.04710259]
 [0.2803951  0.26007378 0.41384506 0.04568605]
 ...
 [0.26076522 0.18916182 0.49945837 0.05061463]
 [0.23828968 0.13883264 0.4906759  0.13220172]
 [0.24481644 0.1395236  0.53283435 0.08282562]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 57.42 [%]
Global accuracy score (test) = 51.07 [%]
Global F1 score (train) = 56.57 [%]
Global F1 score (test) = 50.24 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.41      0.24      0.31       400
MODERATE-INTENSITY       0.46      0.63      0.53       400
         SEDENTARY       0.51      0.67      0.58       400
VIGOROUS-INTENSITY       0.73      0.50      0.60       345

          accuracy                           0.51      1545
         macro avg       0.53      0.51      0.50      1545
      weighted avg       0.52      0.51      0.50      1545

2025-11-05 10:41:43.639875: 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`.
2025-11-05 10:41:43.651242: 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 00:00:1762335703.664423 3003880 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762335703.668763 3003880 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762335703.678533 3003880 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335703.678552 3003880 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335703.678554 3003880 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335703.678556 3003880 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:41:43.681722: 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 00:00:1762335705.938171 3003880 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 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..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/53
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762335707.584265 3004011 service.cc:152] XLA service 0x718cac00c3c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762335707.584322 3004011 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:41:47.617393: 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 00:00:1762335707.784384 3004011 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762335709.983620 3004011 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18:35[0m 3s/step - accuracy: 0.2500 - loss: 2.5071
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2395 - loss: 2.5636  
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2523 - loss: 2.4194
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2591 - loss: 2.3165
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2632 - loss: 2.2349
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2672 - loss: 2.1673
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2709 - loss: 2.1073
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2749 - loss: 2.0508
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2781 - loss: 2.0099
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step - accuracy: 0.2791 - loss: 1.9959
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 10ms/step - accuracy: 0.2792 - loss: 1.9949 - val_accuracy: 0.4055 - val_loss: 1.2769
Epoch 2/53

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[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3419 - loss: 1.3615
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[1m161/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3428 - loss: 1.3622
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Epoch 3/53

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[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3729 - loss: 1.3256
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3733 - loss: 1.3246
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Epoch 4/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4234 - loss: 1.2567 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4161 - loss: 1.2645
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4105 - loss: 1.2688
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4066 - loss: 1.2721
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4042 - loss: 1.2742
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4029 - loss: 1.2752
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4023 - loss: 1.2756
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4018 - loss: 1.2758
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4015 - loss: 1.2758 - val_accuracy: 0.4565 - val_loss: 1.1974
Epoch 5/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4349 - loss: 1.2271 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4242 - loss: 1.2343
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[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4147 - loss: 1.2419
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4123 - loss: 1.2440
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4110 - loss: 1.2451
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4107 - loss: 1.2457
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4106 - loss: 1.2460
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4106 - loss: 1.2461 - val_accuracy: 0.5025 - val_loss: 1.1704
Epoch 6/53

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[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4135 - loss: 1.2429 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4221 - loss: 1.2361
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4239 - loss: 1.2384
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4266 - loss: 1.2381
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[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4291 - loss: 1.2380
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4291 - loss: 1.2383
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4291 - loss: 1.2385 - val_accuracy: 0.5046 - val_loss: 1.1652
Epoch 7/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4525 - loss: 1.2033 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4419 - loss: 1.2145
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4376 - loss: 1.2188
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4354 - loss: 1.2205
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4337 - loss: 1.2214
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[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4332 - loss: 1.2217
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4336 - loss: 1.2211
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4338 - loss: 1.2209 - val_accuracy: 0.5000 - val_loss: 1.1659
Epoch 8/53

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[1m 42/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4030 - loss: 1.2677 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4175 - loss: 1.2469
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4243 - loss: 1.2377
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4269 - loss: 1.2344
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[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4316 - loss: 1.2281
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4333 - loss: 1.2252
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4345 - loss: 1.2231
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Epoch 9/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4014 - loss: 1.2218 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4161 - loss: 1.2133
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4219 - loss: 1.2108
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4261 - loss: 1.2092
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[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4305 - loss: 1.2072
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4316 - loss: 1.2067
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4329 - loss: 1.2062
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4333 - loss: 1.2060 - val_accuracy: 0.5344 - val_loss: 1.1299
Epoch 10/53

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[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4662 - loss: 1.1672 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4684 - loss: 1.1720
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4710 - loss: 1.1713
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4700 - loss: 1.1732
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4691 - loss: 1.1743
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4689 - loss: 1.1747
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4685 - loss: 1.1750
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4676 - loss: 1.1756
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4670 - loss: 1.1761 - val_accuracy: 0.5218 - val_loss: 1.1446
Epoch 11/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1289
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4898 - loss: 1.1341 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4763 - loss: 1.1516
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4742 - loss: 1.1586
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4724 - loss: 1.1626
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4709 - loss: 1.1654
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4699 - loss: 1.1668
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4690 - loss: 1.1678
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4679 - loss: 1.1688
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4675 - loss: 1.1691 - val_accuracy: 0.5281 - val_loss: 1.1281
Epoch 12/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4643 - loss: 1.1808 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4661 - loss: 1.1701
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4662 - loss: 1.1655
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4659 - loss: 1.1649
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4646 - loss: 1.1664
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4647 - loss: 1.1669
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4648 - loss: 1.1671
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4648 - loss: 1.1677
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4648 - loss: 1.1680 - val_accuracy: 0.5225 - val_loss: 1.1413
Epoch 13/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3750 - loss: 1.2191
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4425 - loss: 1.1960 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4504 - loss: 1.1842
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4549 - loss: 1.1792
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4575 - loss: 1.1761
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4596 - loss: 1.1739
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4611 - loss: 1.1723
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4620 - loss: 1.1711
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4627 - loss: 1.1696
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4631 - loss: 1.1688 - val_accuracy: 0.5277 - val_loss: 1.1296
Epoch 14/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4717 - loss: 1.1611 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4708 - loss: 1.1519
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4701 - loss: 1.1489
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4701 - loss: 1.1481
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4696 - loss: 1.1483
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4691 - loss: 1.1493
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4689 - loss: 1.1503
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4688 - loss: 1.1509
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4688 - loss: 1.1512 - val_accuracy: 0.5211 - val_loss: 1.1274
Epoch 15/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.6250 - loss: 1.0082
[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4799 - loss: 1.1277 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4746 - loss: 1.1362
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4717 - loss: 1.1419
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4714 - loss: 1.1439
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4707 - loss: 1.1458
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4707 - loss: 1.1470
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4708 - loss: 1.1482
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4708 - loss: 1.1492
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4707 - loss: 1.1498 - val_accuracy: 0.5446 - val_loss: 1.1200
Epoch 16/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1133
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4802 - loss: 1.1332 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4744 - loss: 1.1358
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4697 - loss: 1.1408
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4694 - loss: 1.1428
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4697 - loss: 1.1432
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4697 - loss: 1.1434
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4702 - loss: 1.1433
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4706 - loss: 1.1434
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4709 - loss: 1.1435 - val_accuracy: 0.5471 - val_loss: 1.1255
Epoch 17/53

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[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4686 - loss: 1.1581 
[1m 66/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4666 - loss: 1.1609
[1m106/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4699 - loss: 1.1569
[1m146/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4720 - loss: 1.1542
[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4723 - loss: 1.1533
[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4724 - loss: 1.1525
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4723 - loss: 1.1525
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4724 - loss: 1.1524
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4726 - loss: 1.1521 - val_accuracy: 0.5442 - val_loss: 1.1150
Epoch 18/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6875 - loss: 1.0037
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4903 - loss: 1.1696 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4814 - loss: 1.1608
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4751 - loss: 1.1574
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4724 - loss: 1.1554
[1m201/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4716 - loss: 1.1531
[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4713 - loss: 1.1511
[1m282/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4712 - loss: 1.1498
[1m322/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4715 - loss: 1.1485
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4715 - loss: 1.1483 - val_accuracy: 0.5414 - val_loss: 1.1180
Epoch 19/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4869 - loss: 1.1237 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4903 - loss: 1.1212
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4879 - loss: 1.1235
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4870 - loss: 1.1240
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4859 - loss: 1.1249
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4854 - loss: 1.1251
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4853 - loss: 1.1249
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4851 - loss: 1.1250
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4849 - loss: 1.1253 - val_accuracy: 0.5523 - val_loss: 1.1155
Epoch 20/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.0972
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4720 - loss: 1.1437 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4778 - loss: 1.1369
[1m109/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4801 - loss: 1.1341
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4808 - loss: 1.1318
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4810 - loss: 1.1306
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4817 - loss: 1.1294
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4825 - loss: 1.1285
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4831 - loss: 1.1286
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4832 - loss: 1.1287 - val_accuracy: 0.5569 - val_loss: 1.1031
Epoch 21/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 0.8425
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4783 - loss: 1.1187 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4846 - loss: 1.1190
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4862 - loss: 1.1216
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4874 - loss: 1.1229
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4888 - loss: 1.1226
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4896 - loss: 1.1229
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4902 - loss: 1.1227
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4904 - loss: 1.1228
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4903 - loss: 1.1229 - val_accuracy: 0.5657 - val_loss: 1.1015
Epoch 22/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4665 - loss: 1.1121 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4711 - loss: 1.1220
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4788 - loss: 1.1210
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4831 - loss: 1.1211
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4851 - loss: 1.1213
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4862 - loss: 1.1215
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4872 - loss: 1.1209
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4881 - loss: 1.1205
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4885 - loss: 1.1203 - val_accuracy: 0.5614 - val_loss: 1.1014
Epoch 23/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4062 - loss: 1.1703
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4882 - loss: 1.1434 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4970 - loss: 1.1303
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5020 - loss: 1.1206
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5038 - loss: 1.1175
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5050 - loss: 1.1154
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5056 - loss: 1.1136
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5058 - loss: 1.1126
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5057 - loss: 1.1121
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5057 - loss: 1.1119 - val_accuracy: 0.5639 - val_loss: 1.1044
Epoch 24/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.6875 - loss: 0.8784
[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5048 - loss: 1.0830 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5041 - loss: 1.0924
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5031 - loss: 1.0962
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5022 - loss: 1.0994
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5021 - loss: 1.1005
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5020 - loss: 1.1015
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5017 - loss: 1.1026
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5014 - loss: 1.1034
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5011 - loss: 1.1038 - val_accuracy: 0.5593 - val_loss: 1.0980
Epoch 25/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5625 - loss: 1.0267
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5093 - loss: 1.0745 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5064 - loss: 1.0825
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5065 - loss: 1.0842
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5058 - loss: 1.0858
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5050 - loss: 1.0873
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5041 - loss: 1.0890
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5035 - loss: 1.0901
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5029 - loss: 1.0913
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5025 - loss: 1.0920 - val_accuracy: 0.5485 - val_loss: 1.0986
Epoch 26/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.0754
[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4926 - loss: 1.1112 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4983 - loss: 1.1072
[1m109/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5042 - loss: 1.1000
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5075 - loss: 1.0965
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5081 - loss: 1.0975
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5085 - loss: 1.0978
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5088 - loss: 1.0978
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5086 - loss: 1.0985
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5084 - loss: 1.0991 - val_accuracy: 0.5646 - val_loss: 1.1072
Epoch 27/53

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[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4778 - loss: 1.1072 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4839 - loss: 1.1066
[1m108/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4870 - loss: 1.1042
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4912 - loss: 1.0998
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4934 - loss: 1.0981
[1m224/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4946 - loss: 1.0967
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4951 - loss: 1.0963
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4954 - loss: 1.0963
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4956 - loss: 1.0966 - val_accuracy: 0.5678 - val_loss: 1.0891
Epoch 28/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 0.9512
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5088 - loss: 1.0964 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5129 - loss: 1.0909
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5124 - loss: 1.0893
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5117 - loss: 1.0880
[1m199/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5110 - loss: 1.0878
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5105 - loss: 1.0877
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5105 - loss: 1.0877
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5101 - loss: 1.0883
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5099 - loss: 1.0885 - val_accuracy: 0.5657 - val_loss: 1.1164
Epoch 29/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4598 - loss: 1.0915 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4757 - loss: 1.0878
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4861 - loss: 1.0832
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4905 - loss: 1.0829
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4940 - loss: 1.0829
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4961 - loss: 1.0832
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4975 - loss: 1.0836
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4985 - loss: 1.0838
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4989 - loss: 1.0838 - val_accuracy: 0.5537 - val_loss: 1.1027
Epoch 30/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4062 - loss: 1.1979
[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4999 - loss: 1.0823 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5136 - loss: 1.0708
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5162 - loss: 1.0734
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5169 - loss: 1.0753
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5173 - loss: 1.0755
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5169 - loss: 1.0764
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5168 - loss: 1.0769
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5168 - loss: 1.0775
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5168 - loss: 1.0778 - val_accuracy: 0.5825 - val_loss: 1.1054
Epoch 31/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6562 - loss: 1.0297
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5333 - loss: 1.0507 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5297 - loss: 1.0575
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5278 - loss: 1.0599
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5263 - loss: 1.0614
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5253 - loss: 1.0629
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5252 - loss: 1.0635
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5252 - loss: 1.0638
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5250 - loss: 1.0643
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5248 - loss: 1.0647 - val_accuracy: 0.5597 - val_loss: 1.0941
Epoch 32/53

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 397ms/step
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Saved model to disk.

Accuracy capturado en la ejecución 5: 51.07 [%]
F1-score capturado en la ejecución 5: 50.24 [%]

=== EJECUCIÓN 6 ===

--- TRAIN (ejecución 6) ---

--- TEST (ejecución 6) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:20[0m 979ms/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m65/89[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 786us/step
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Global accuracy score (validation) = 55.27 [%]
Global F1 score (validation) = 54.34 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.23427325 0.15318996 0.5420999  0.07043692]
 [0.3051215  0.32998717 0.17496662 0.18992467]
 [0.1947471  0.15539649 0.59517884 0.05467755]
 ...
 [0.22832921 0.18292889 0.5129099  0.07583199]
 [0.26340035 0.17276521 0.41956574 0.14426862]
 [0.26684514 0.19322157 0.49696523 0.04296802]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 55.51 [%]
Global accuracy score (test) = 51.13 [%]
Global F1 score (train) = 55.47 [%]
Global F1 score (test) = 50.81 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.43      0.32      0.36       400
MODERATE-INTENSITY       0.46      0.62      0.53       400
         SEDENTARY       0.51      0.64      0.57       400
VIGOROUS-INTENSITY       0.75      0.46      0.57       345

          accuracy                           0.51      1545
         macro avg       0.54      0.51      0.51      1545
      weighted avg       0.53      0.51      0.51      1545

2025-11-05 10:42:22.311868: 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`.
2025-11-05 10:42:22.323084: 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 00:00:1762335742.336461 3007839 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762335742.340583 3007839 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762335742.350407 3007839 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335742.350429 3007839 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335742.350431 3007839 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335742.350433 3007839 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:42:22.353647: 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 00:00:1762335744.594795 3007839 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 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..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/53
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762335746.236019 3007972 service.cc:152] XLA service 0x7c6ce0005b20 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762335746.236054 3007972 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:42:26.269355: 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 00:00:1762335746.436421 3007972 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762335748.604147 3007972 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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[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2899 - loss: 2.0617
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2915 - loss: 2.0159
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Epoch 2/53

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[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3283 - loss: 1.3817
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[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3326 - loss: 1.3714
[1m222/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3339 - loss: 1.3694
[1m258/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3350 - loss: 1.3678
[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3362 - loss: 1.3661
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Epoch 3/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3890 - loss: 1.2870 
[1m 69/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3785 - loss: 1.2963
[1m108/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3761 - loss: 1.3017
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3752 - loss: 1.3043
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[1m223/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3759 - loss: 1.3046
[1m262/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3759 - loss: 1.3049
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3757 - loss: 1.3051
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Epoch 4/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3881 - loss: 1.2879 
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[1m110/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3842 - loss: 1.2905
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3833 - loss: 1.2925
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3834 - loss: 1.2925
[1m224/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3841 - loss: 1.2918
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3847 - loss: 1.2911
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3859 - loss: 1.2903
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Epoch 5/53

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[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3984 - loss: 1.2722
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[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4048 - loss: 1.2686
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Epoch 6/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4327 - loss: 1.2238 
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[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4207 - loss: 1.2429
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4206 - loss: 1.2433
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Epoch 7/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4189 - loss: 1.2005 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4234 - loss: 1.2096
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[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4263 - loss: 1.2139
[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4263 - loss: 1.2154
[1m224/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4262 - loss: 1.2170
[1m260/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4262 - loss: 1.2181
[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4263 - loss: 1.2188
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4265 - loss: 1.2190 - val_accuracy: 0.5137 - val_loss: 1.1680
Epoch 8/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4226 - loss: 1.2388 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4340 - loss: 1.2215
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4360 - loss: 1.2181
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4358 - loss: 1.2173
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4352 - loss: 1.2174
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4345 - loss: 1.2175
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4343 - loss: 1.2174
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4346 - loss: 1.2166
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4346 - loss: 1.2162 - val_accuracy: 0.4898 - val_loss: 1.1564
Epoch 9/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4642 - loss: 1.1813 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4602 - loss: 1.1891
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4596 - loss: 1.1924
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4576 - loss: 1.1945
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4569 - loss: 1.1954
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4560 - loss: 1.1957
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4552 - loss: 1.1956
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4544 - loss: 1.1953
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4541 - loss: 1.1952 - val_accuracy: 0.5176 - val_loss: 1.1507
Epoch 10/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4750 - loss: 1.1650 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4626 - loss: 1.1794
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4602 - loss: 1.1835
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4592 - loss: 1.1854
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4583 - loss: 1.1867
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4576 - loss: 1.1881
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4570 - loss: 1.1888
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4567 - loss: 1.1890
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4566 - loss: 1.1891 - val_accuracy: 0.5049 - val_loss: 1.1495
Epoch 11/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4679 - loss: 1.1794 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4632 - loss: 1.1840
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4594 - loss: 1.1839
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4585 - loss: 1.1824
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4590 - loss: 1.1806
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4598 - loss: 1.1799
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4605 - loss: 1.1794
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4611 - loss: 1.1792
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4613 - loss: 1.1792 - val_accuracy: 0.4982 - val_loss: 1.1572
Epoch 12/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4369 - loss: 1.2021 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4491 - loss: 1.1922
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4522 - loss: 1.1886
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4541 - loss: 1.1866
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4563 - loss: 1.1848
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4579 - loss: 1.1834
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4589 - loss: 1.1823
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4596 - loss: 1.1815
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4598 - loss: 1.1811 - val_accuracy: 0.5144 - val_loss: 1.1566
Epoch 13/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 1.0499
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4872 - loss: 1.1403 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4802 - loss: 1.1548
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4752 - loss: 1.1615
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4716 - loss: 1.1649
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4697 - loss: 1.1661
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4687 - loss: 1.1663
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4684 - loss: 1.1663
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4683 - loss: 1.1663
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4683 - loss: 1.1663 - val_accuracy: 0.5091 - val_loss: 1.1384
Epoch 14/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 1.1836
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4742 - loss: 1.1686 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4709 - loss: 1.1713
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4690 - loss: 1.1724
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4685 - loss: 1.1720
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4678 - loss: 1.1720
[1m226/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4683 - loss: 1.1711
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4687 - loss: 1.1701
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4689 - loss: 1.1693
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4691 - loss: 1.1688 - val_accuracy: 0.5154 - val_loss: 1.1433
Epoch 15/53

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[1m 42/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4521 - loss: 1.1455 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4557 - loss: 1.1504
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4599 - loss: 1.1527
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4619 - loss: 1.1537
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4635 - loss: 1.1539
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[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4650 - loss: 1.1548
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4653 - loss: 1.1555 - val_accuracy: 0.5140 - val_loss: 1.1447
Epoch 16/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4947 - loss: 1.1228 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4873 - loss: 1.1319
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4846 - loss: 1.1354
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4841 - loss: 1.1374
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[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4829 - loss: 1.1406
[1m262/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4825 - loss: 1.1414
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4826 - loss: 1.1411 - val_accuracy: 0.5291 - val_loss: 1.1429
Epoch 17/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4841 - loss: 1.1377 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4822 - loss: 1.1467
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4801 - loss: 1.1506
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4794 - loss: 1.1511
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[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4790 - loss: 1.1518
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4789 - loss: 1.1521
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4787 - loss: 1.1523
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4788 - loss: 1.1522 - val_accuracy: 0.5316 - val_loss: 1.1265
Epoch 18/53

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[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4852 - loss: 1.1611 
[1m 71/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4748 - loss: 1.1520
[1m109/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4722 - loss: 1.1485
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4733 - loss: 1.1442
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4751 - loss: 1.1415
[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4766 - loss: 1.1395
[1m263/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4774 - loss: 1.1384
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4779 - loss: 1.1372
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4782 - loss: 1.1367 - val_accuracy: 0.5463 - val_loss: 1.1349
Epoch 19/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5078 - loss: 1.0979 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5007 - loss: 1.1060
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4980 - loss: 1.1119
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4963 - loss: 1.1164
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4951 - loss: 1.1200
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4947 - loss: 1.1219
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4944 - loss: 1.1231
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4935 - loss: 1.1245
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4930 - loss: 1.1253 - val_accuracy: 0.5421 - val_loss: 1.1255
Epoch 20/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4636 - loss: 1.1508 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4732 - loss: 1.1423
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4787 - loss: 1.1365
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4827 - loss: 1.1327
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4848 - loss: 1.1308
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4851 - loss: 1.1303
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4852 - loss: 1.1297
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4856 - loss: 1.1289
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4856 - loss: 1.1288 - val_accuracy: 0.5495 - val_loss: 1.1239
Epoch 21/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.1039
[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4749 - loss: 1.1172 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4853 - loss: 1.1147
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4879 - loss: 1.1180
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4883 - loss: 1.1199
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4883 - loss: 1.1211
[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4885 - loss: 1.1220
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4891 - loss: 1.1221
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4896 - loss: 1.1217
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4897 - loss: 1.1218 - val_accuracy: 0.5474 - val_loss: 1.1277
Epoch 22/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 1.0451
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4935 - loss: 1.1207 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4869 - loss: 1.1292
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4851 - loss: 1.1323
[1m162/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4856 - loss: 1.1319
[1m202/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4861 - loss: 1.1307
[1m243/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4876 - loss: 1.1283
[1m281/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4883 - loss: 1.1270
[1m323/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4890 - loss: 1.1258
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4890 - loss: 1.1257 - val_accuracy: 0.5481 - val_loss: 1.1235
Epoch 23/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4062 - loss: 1.1362
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5108 - loss: 1.0845 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5095 - loss: 1.0891
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5087 - loss: 1.0933
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5074 - loss: 1.0966
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5065 - loss: 1.0977
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5054 - loss: 1.0992
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5044 - loss: 1.1005
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5038 - loss: 1.1015
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5034 - loss: 1.1021 - val_accuracy: 0.5316 - val_loss: 1.1346
Epoch 24/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 1.0900
[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5122 - loss: 1.1034 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5079 - loss: 1.1028
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5066 - loss: 1.1070
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5049 - loss: 1.1084
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5040 - loss: 1.1088
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5042 - loss: 1.1089
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5042 - loss: 1.1091
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5043 - loss: 1.1094
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5042 - loss: 1.1096 - val_accuracy: 0.5537 - val_loss: 1.1190
Epoch 25/53

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[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5168 - loss: 1.1160 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5099 - loss: 1.1074
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5080 - loss: 1.1062
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5068 - loss: 1.1063
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5052 - loss: 1.1064
[1m224/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5046 - loss: 1.1059
[1m259/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5043 - loss: 1.1057
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5042 - loss: 1.1054
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5041 - loss: 1.1053 - val_accuracy: 0.5604 - val_loss: 1.1218
Epoch 26/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 0.9828
[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4746 - loss: 1.1026 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4869 - loss: 1.1040
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4900 - loss: 1.1049
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4918 - loss: 1.1055
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4927 - loss: 1.1070
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4938 - loss: 1.1074
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4949 - loss: 1.1076
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4961 - loss: 1.1076
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4967 - loss: 1.1075 - val_accuracy: 0.5593 - val_loss: 1.1201
Epoch 27/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3750 - loss: 1.2290
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5136 - loss: 1.1310 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5096 - loss: 1.1305
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5073 - loss: 1.1270
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5063 - loss: 1.1242
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5055 - loss: 1.1220
[1m226/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5056 - loss: 1.1193
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5059 - loss: 1.1169
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5066 - loss: 1.1145
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5070 - loss: 1.1131 - val_accuracy: 0.5667 - val_loss: 1.0979
Epoch 28/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.5000 - loss: 1.1356
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4832 - loss: 1.1303 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4872 - loss: 1.1163
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4907 - loss: 1.1084
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4940 - loss: 1.1038
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4962 - loss: 1.1021
[1m223/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4976 - loss: 1.1016
[1m257/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4983 - loss: 1.1017
[1m296/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4990 - loss: 1.1017
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4996 - loss: 1.1011 - val_accuracy: 0.5629 - val_loss: 1.0913
Epoch 29/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5938 - loss: 0.9978
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5224 - loss: 1.0883 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5151 - loss: 1.0989
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5146 - loss: 1.1004
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5151 - loss: 1.0990
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5152 - loss: 1.0974
[1m217/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5153 - loss: 1.0965
[1m258/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5152 - loss: 1.0956
[1m293/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5152 - loss: 1.0946
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5152 - loss: 1.0935 - val_accuracy: 0.5706 - val_loss: 1.1009
Epoch 30/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5144 - loss: 1.0463 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5115 - loss: 1.0632
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5114 - loss: 1.0695
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5118 - loss: 1.0728
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5117 - loss: 1.0756
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5121 - loss: 1.0775
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5128 - loss: 1.0782
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5132 - loss: 1.0790
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5134 - loss: 1.0792 - val_accuracy: 0.5758 - val_loss: 1.1166
Epoch 31/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4062 - loss: 1.1660
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5035 - loss: 1.0532 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5090 - loss: 1.0564
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5100 - loss: 1.0615
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5089 - loss: 1.0664
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5080 - loss: 1.0694
[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5075 - loss: 1.0716
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5074 - loss: 1.0734
[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5076 - loss: 1.0742
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5081 - loss: 1.0745 - val_accuracy: 0.5614 - val_loss: 1.1087
Epoch 32/53

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[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4919 - loss: 1.1138 
[1m 71/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5015 - loss: 1.1009
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5049 - loss: 1.0985
[1m145/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5071 - loss: 1.0959
[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5094 - loss: 1.0936
[1m224/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5114 - loss: 1.0912
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5128 - loss: 1.0894
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5137 - loss: 1.0885
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5141 - loss: 1.0883 - val_accuracy: 0.5614 - val_loss: 1.0965
Epoch 33/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5938 - loss: 0.9104
[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5210 - loss: 1.0628 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5137 - loss: 1.0733
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5166 - loss: 1.0740
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5179 - loss: 1.0756
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5198 - loss: 1.0750
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5215 - loss: 1.0736
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5220 - loss: 1.0729
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5221 - loss: 1.0732
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5220 - loss: 1.0735 - val_accuracy: 0.5569 - val_loss: 1.0910
Epoch 34/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3438 - loss: 1.2373
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4877 - loss: 1.0961 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5045 - loss: 1.0865
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5113 - loss: 1.0813
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5137 - loss: 1.0803
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5157 - loss: 1.0795
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5168 - loss: 1.0789
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5179 - loss: 1.0779
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5185 - loss: 1.0772
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5188 - loss: 1.0769 - val_accuracy: 0.5629 - val_loss: 1.1015
Epoch 35/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4999 - loss: 1.1066 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5111 - loss: 1.0919
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5148 - loss: 1.0853
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5188 - loss: 1.0807
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[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5220 - loss: 1.0762
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5228 - loss: 1.0751
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5236 - loss: 1.0739
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5239 - loss: 1.0735 - val_accuracy: 0.5481 - val_loss: 1.1088
Epoch 36/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5439 - loss: 1.0484 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5421 - loss: 1.0451
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[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5404 - loss: 1.0462
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[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5389 - loss: 1.0497
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5383 - loss: 1.0514
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5376 - loss: 1.0527
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5372 - loss: 1.0534 - val_accuracy: 0.5558 - val_loss: 1.0946
Epoch 37/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5422 - loss: 1.0263 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5295 - loss: 1.0389
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5297 - loss: 1.0413
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5306 - loss: 1.0432
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5312 - loss: 1.0450
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5314 - loss: 1.0462
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5311 - loss: 1.0479
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5307 - loss: 1.0497
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5306 - loss: 1.0508 - val_accuracy: 0.5650 - val_loss: 1.0859
Epoch 38/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0225
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5255 - loss: 1.0419 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5285 - loss: 1.0525
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5276 - loss: 1.0568
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5281 - loss: 1.0593
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5293 - loss: 1.0602
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5305 - loss: 1.0602
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5312 - loss: 1.0600
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5318 - loss: 1.0595
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5321 - loss: 1.0591 - val_accuracy: 0.5748 - val_loss: 1.0977
Epoch 39/53

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[1m 42/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5187 - loss: 1.0590 
[1m 82/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5246 - loss: 1.0585
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5291 - loss: 1.0578
[1m161/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5318 - loss: 1.0576
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[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5331 - loss: 1.0582
[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5329 - loss: 1.0587
[1m319/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5327 - loss: 1.0587
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Epoch 40/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5180 - loss: 1.0515 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5268 - loss: 1.0510
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[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5328 - loss: 1.0482
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[1m263/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5324 - loss: 1.0505
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Epoch 41/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0929
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5334 - loss: 1.0380 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5385 - loss: 1.0349
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5368 - loss: 1.0387
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5359 - loss: 1.0399
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5352 - loss: 1.0407
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[1m262/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5345 - loss: 1.0422
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Epoch 42/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.6250 - loss: 1.0397
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 403ms/step
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Saved model to disk.

Accuracy capturado en la ejecución 6: 51.13 [%]
F1-score capturado en la ejecución 6: 50.81 [%]

=== EJECUCIÓN 7 ===

--- TRAIN (ejecución 7) ---

--- TEST (ejecución 7) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 14ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m59/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 865us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 941us/step
Global accuracy score (validation) = 57.37 [%]
Global F1 score (validation) = 56.63 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.09620936 0.14021504 0.7568727  0.00670285]
 [0.23982179 0.20695193 0.52026033 0.03296593]
 [0.08061233 0.12670882 0.7866529  0.00602597]
 ...
 [0.24425526 0.1650153  0.5079155  0.08281399]
 [0.25440568 0.15672733 0.47240654 0.11646034]
 [0.2607268  0.17268725 0.48957887 0.07700706]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 58.15 [%]
Global accuracy score (test) = 51.91 [%]
Global F1 score (train) = 58.26 [%]
Global F1 score (test) = 51.43 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.41      0.29      0.34       400
MODERATE-INTENSITY       0.47      0.60      0.53       400
         SEDENTARY       0.52      0.70      0.60       400
VIGOROUS-INTENSITY       0.79      0.48      0.59       345

          accuracy                           0.52      1545
         macro avg       0.55      0.52      0.51      1545
      weighted avg       0.54      0.52      0.51      1545

2025-11-05 10:43:07.232771: 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`.
2025-11-05 10:43:07.244084: 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 00:00:1762335787.257472 3012738 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762335787.261632 3012738 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762335787.271806 3012738 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335787.271826 3012738 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335787.271828 3012738 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335787.271829 3012738 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:43:07.275141: 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 00:00:1762335789.503068 3012738 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 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..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/53
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762335791.094025 3012872 service.cc:152] XLA service 0x7ccae800d1d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762335791.094057 3012872 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:43:11.127187: 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 00:00:1762335791.293632 3012872 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762335793.461577 3012872 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18:11[0m 3s/step - accuracy: 0.2812 - loss: 2.9552
[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2510 - loss: 2.6764  
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2579 - loss: 2.5221
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2636 - loss: 2.3994
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2686 - loss: 2.3030
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2723 - loss: 2.2259
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2754 - loss: 2.1553
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2781 - loss: 2.0977
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2805 - loss: 2.0499
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step - accuracy: 0.2813 - loss: 2.0358
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 10ms/step - accuracy: 0.2813 - loss: 2.0347 - val_accuracy: 0.4635 - val_loss: 1.2636
Epoch 2/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.2188 - loss: 1.4826
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3536 - loss: 1.3879 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3494 - loss: 1.3861
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3492 - loss: 1.3853
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3493 - loss: 1.3827
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3489 - loss: 1.3802
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3490 - loss: 1.3774
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3494 - loss: 1.3749
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3497 - loss: 1.3728
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3499 - loss: 1.3718 - val_accuracy: 0.4944 - val_loss: 1.2440
Epoch 3/53

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[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3719 - loss: 1.3265
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Epoch 4/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4039 - loss: 1.3039 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3944 - loss: 1.3015
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Epoch 5/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4097 - loss: 1.2348 
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[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4043 - loss: 1.2563
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[1m221/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4051 - loss: 1.2592
[1m261/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4054 - loss: 1.2597
[1m296/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4057 - loss: 1.2599
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4060 - loss: 1.2600 - val_accuracy: 0.5130 - val_loss: 1.1834
Epoch 6/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4063 - loss: 1.2193 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4167 - loss: 1.2300
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[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4250 - loss: 1.2335
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4267 - loss: 1.2347
[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4269 - loss: 1.2362
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4270 - loss: 1.2374
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4274 - loss: 1.2379
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4276 - loss: 1.2380 - val_accuracy: 0.5011 - val_loss: 1.1914
Epoch 7/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4171 - loss: 1.2376 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4203 - loss: 1.2393
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[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4227 - loss: 1.2388
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[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4241 - loss: 1.2378
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4248 - loss: 1.2372
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4256 - loss: 1.2364
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Epoch 8/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4585 - loss: 1.2345 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4511 - loss: 1.2364
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4445 - loss: 1.2370
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4427 - loss: 1.2342
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4426 - loss: 1.2311
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4423 - loss: 1.2288
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4417 - loss: 1.2272
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4413 - loss: 1.2257
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4412 - loss: 1.2252 - val_accuracy: 0.5140 - val_loss: 1.1595
Epoch 9/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 1.1341
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4459 - loss: 1.2120 
[1m 71/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4447 - loss: 1.2139
[1m110/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4463 - loss: 1.2138
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4479 - loss: 1.2120
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4489 - loss: 1.2101
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4498 - loss: 1.2081
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4500 - loss: 1.2065
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4499 - loss: 1.2056
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4498 - loss: 1.2053 - val_accuracy: 0.5137 - val_loss: 1.1571
Epoch 10/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4710 - loss: 1.1849 
[1m 70/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4691 - loss: 1.1817
[1m106/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4655 - loss: 1.1837
[1m146/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4620 - loss: 1.1876
[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4594 - loss: 1.1903
[1m223/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4584 - loss: 1.1908
[1m261/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4581 - loss: 1.1908
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4581 - loss: 1.1907
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4580 - loss: 1.1908 - val_accuracy: 0.5263 - val_loss: 1.1396
Epoch 11/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.4375 - loss: 1.2326
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5045 - loss: 1.1523 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4945 - loss: 1.1664
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4859 - loss: 1.1735
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4809 - loss: 1.1770
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4769 - loss: 1.1790
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4741 - loss: 1.1797
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4727 - loss: 1.1797
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4717 - loss: 1.1798
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4711 - loss: 1.1799 - val_accuracy: 0.5154 - val_loss: 1.1510
Epoch 12/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.2607
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4564 - loss: 1.1790 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4600 - loss: 1.1797
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4626 - loss: 1.1779
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4639 - loss: 1.1768
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4645 - loss: 1.1763
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4648 - loss: 1.1756
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4649 - loss: 1.1752
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4649 - loss: 1.1751
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4649 - loss: 1.1751 - val_accuracy: 0.5341 - val_loss: 1.1286
Epoch 13/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4606 - loss: 1.2030 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4664 - loss: 1.1856
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4683 - loss: 1.1783
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4674 - loss: 1.1757
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[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4664 - loss: 1.1746
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4665 - loss: 1.1744 - val_accuracy: 0.5379 - val_loss: 1.1303
Epoch 14/53

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[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4836 - loss: 1.1595 
[1m 71/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4815 - loss: 1.1692
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[1m142/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4796 - loss: 1.1710
[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4788 - loss: 1.1707
[1m220/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4781 - loss: 1.1707
[1m259/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4775 - loss: 1.1708
[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4773 - loss: 1.1706
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4775 - loss: 1.1702 - val_accuracy: 0.5369 - val_loss: 1.1321
Epoch 15/53

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[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4531 - loss: 1.2046 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4606 - loss: 1.1890
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4630 - loss: 1.1831
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4643 - loss: 1.1800
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4656 - loss: 1.1779
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4668 - loss: 1.1767
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4672 - loss: 1.1762
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4674 - loss: 1.1758
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4677 - loss: 1.1754 - val_accuracy: 0.5256 - val_loss: 1.1301
Epoch 16/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.0188
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5169 - loss: 1.1310 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5012 - loss: 1.1415
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4947 - loss: 1.1460
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4912 - loss: 1.1485
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4889 - loss: 1.1498
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4870 - loss: 1.1508
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4855 - loss: 1.1517
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4843 - loss: 1.1524
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4836 - loss: 1.1527 - val_accuracy: 0.5432 - val_loss: 1.1335
Epoch 17/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4443 - loss: 1.1294 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4518 - loss: 1.1386
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4574 - loss: 1.1414
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4635 - loss: 1.1415
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4666 - loss: 1.1418
[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4684 - loss: 1.1420
[1m262/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4700 - loss: 1.1420
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4711 - loss: 1.1422
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4718 - loss: 1.1425 - val_accuracy: 0.5421 - val_loss: 1.1272
Epoch 18/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4579 - loss: 1.1662 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4622 - loss: 1.1615
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4649 - loss: 1.1607
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4670 - loss: 1.1596
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4685 - loss: 1.1583
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4696 - loss: 1.1574
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4711 - loss: 1.1560
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4723 - loss: 1.1548
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4728 - loss: 1.1543 - val_accuracy: 0.5485 - val_loss: 1.1280
Epoch 19/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4688 - loss: 1.0795
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5080 - loss: 1.1433 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5062 - loss: 1.1458
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5032 - loss: 1.1445
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5032 - loss: 1.1423
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5016 - loss: 1.1429
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5000 - loss: 1.1437
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4987 - loss: 1.1438
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4982 - loss: 1.1435
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4979 - loss: 1.1432 - val_accuracy: 0.5478 - val_loss: 1.1166
Epoch 20/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4823 - loss: 1.1488 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4869 - loss: 1.1387
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4877 - loss: 1.1357
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4876 - loss: 1.1345
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4881 - loss: 1.1337
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4883 - loss: 1.1334
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4886 - loss: 1.1330
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4887 - loss: 1.1330
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4888 - loss: 1.1331 - val_accuracy: 0.5650 - val_loss: 1.1000
Epoch 21/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.2365
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4896 - loss: 1.1150 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4853 - loss: 1.1209
[1m110/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4835 - loss: 1.1233
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4830 - loss: 1.1246
[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4835 - loss: 1.1252
[1m222/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4839 - loss: 1.1256
[1m261/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4845 - loss: 1.1258
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4851 - loss: 1.1261
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4855 - loss: 1.1264 - val_accuracy: 0.5643 - val_loss: 1.1025
Epoch 22/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5938 - loss: 1.0021
[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5406 - loss: 1.0803 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5258 - loss: 1.0888
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5207 - loss: 1.0932
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5181 - loss: 1.0965
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5165 - loss: 1.0987
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5148 - loss: 1.1013
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5130 - loss: 1.1037
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5114 - loss: 1.1058
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5107 - loss: 1.1069 - val_accuracy: 0.5579 - val_loss: 1.1090
Epoch 23/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5011 - loss: 1.1267 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4977 - loss: 1.1208
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4967 - loss: 1.1166
[1m163/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4964 - loss: 1.1157
[1m202/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4954 - loss: 1.1170
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4944 - loss: 1.1186
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4940 - loss: 1.1198
[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4941 - loss: 1.1202
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4941 - loss: 1.1202 - val_accuracy: 0.5478 - val_loss: 1.1038
Epoch 24/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1455
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5012 - loss: 1.1081 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4978 - loss: 1.1147
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5007 - loss: 1.1136
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5028 - loss: 1.1129
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5021 - loss: 1.1130
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5014 - loss: 1.1138
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5009 - loss: 1.1145
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5009 - loss: 1.1144
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5008 - loss: 1.1143 - val_accuracy: 0.5555 - val_loss: 1.0942
Epoch 25/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6250 - loss: 1.0074
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5292 - loss: 1.0724 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5243 - loss: 1.0771
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5242 - loss: 1.0781
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5241 - loss: 1.0791
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5236 - loss: 1.0819
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5225 - loss: 1.0853
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5215 - loss: 1.0883
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5205 - loss: 1.0908
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5200 - loss: 1.0919 - val_accuracy: 0.5541 - val_loss: 1.0990
Epoch 26/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5625 - loss: 0.9654
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5375 - loss: 1.0802 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5291 - loss: 1.0964
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5237 - loss: 1.1033
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5201 - loss: 1.1055
[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5180 - loss: 1.1065
[1m219/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5161 - loss: 1.1068
[1m258/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5149 - loss: 1.1066
[1m296/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5141 - loss: 1.1065
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5135 - loss: 1.1065 - val_accuracy: 0.5460 - val_loss: 1.0917
Epoch 27/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5625 - loss: 1.0175
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5009 - loss: 1.1021 
[1m 71/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5024 - loss: 1.1080
[1m110/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5070 - loss: 1.1049
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5082 - loss: 1.1044
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5085 - loss: 1.1037
[1m223/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5086 - loss: 1.1028
[1m258/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5088 - loss: 1.1020
[1m295/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5090 - loss: 1.1014
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5090 - loss: 1.1012 - val_accuracy: 0.5706 - val_loss: 1.0986
Epoch 28/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5020 - loss: 1.0761 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4995 - loss: 1.0888
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5009 - loss: 1.0911
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5021 - loss: 1.0937
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5031 - loss: 1.0937
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5039 - loss: 1.0932
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5047 - loss: 1.0929
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5055 - loss: 1.0927
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5062 - loss: 1.0925 - val_accuracy: 0.5579 - val_loss: 1.0895
Epoch 29/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.0843
[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5038 - loss: 1.0932 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5096 - loss: 1.0889
[1m110/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5127 - loss: 1.0858
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5139 - loss: 1.0850
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5141 - loss: 1.0850
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5142 - loss: 1.0856
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5145 - loss: 1.0860
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5150 - loss: 1.0861
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5153 - loss: 1.0862 - val_accuracy: 0.5730 - val_loss: 1.0765
Epoch 30/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6562 - loss: 0.9460
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5430 - loss: 1.0681 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5330 - loss: 1.0706
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5273 - loss: 1.0748
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5242 - loss: 1.0781
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5222 - loss: 1.0812
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5214 - loss: 1.0826
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5206 - loss: 1.0841
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5198 - loss: 1.0856
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5195 - loss: 1.0862 - val_accuracy: 0.5706 - val_loss: 1.0846
Epoch 31/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 1.0174
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5412 - loss: 1.0774 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5375 - loss: 1.0715
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5351 - loss: 1.0725
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5341 - loss: 1.0730
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5329 - loss: 1.0741
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5317 - loss: 1.0757
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5307 - loss: 1.0771
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5295 - loss: 1.0784
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5291 - loss: 1.0789 - val_accuracy: 0.5618 - val_loss: 1.0701
Epoch 32/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 1.0794
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5154 - loss: 1.0710 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5238 - loss: 1.0619
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5240 - loss: 1.0624
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5232 - loss: 1.0648
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5226 - loss: 1.0673
[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5219 - loss: 1.0705
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5212 - loss: 1.0728
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5210 - loss: 1.0742
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5210 - loss: 1.0747 - val_accuracy: 0.5688 - val_loss: 1.0751
Epoch 33/53

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[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5115 - loss: 1.0796 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5146 - loss: 1.0760
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5152 - loss: 1.0778
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5182 - loss: 1.0778
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5201 - loss: 1.0783
[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5210 - loss: 1.0792
[1m261/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5212 - loss: 1.0804
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5215 - loss: 1.0816
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5217 - loss: 1.0823 - val_accuracy: 0.5709 - val_loss: 1.0794
Epoch 34/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 0.9834
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5694 - loss: 1.0117 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5597 - loss: 1.0300
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[1m163/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5489 - loss: 1.0455
[1m202/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5465 - loss: 1.0491
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5446 - loss: 1.0516
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5428 - loss: 1.0538
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5410 - loss: 1.0559
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5403 - loss: 1.0569 - val_accuracy: 0.5762 - val_loss: 1.0804
Epoch 35/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.2236
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4727 - loss: 1.1159 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4903 - loss: 1.1011
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5018 - loss: 1.0917
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5072 - loss: 1.0880
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5107 - loss: 1.0858
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5130 - loss: 1.0848
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5149 - loss: 1.0839
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5163 - loss: 1.0834
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5168 - loss: 1.0832 - val_accuracy: 0.5636 - val_loss: 1.0851
Epoch 36/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 1.0834
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5249 - loss: 1.1121 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5294 - loss: 1.0996
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[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5342 - loss: 1.0883
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[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5340 - loss: 1.0820
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5341 - loss: 1.0812 - val_accuracy: 0.5720 - val_loss: 1.0777

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 404ms/step
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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 17ms/step
Saved model to disk.

Accuracy capturado en la ejecución 7: 51.91 [%]
F1-score capturado en la ejecución 7: 51.43 [%]

=== EJECUCIÓN 8 ===

--- TRAIN (ejecución 8) ---

--- TEST (ejecución 8) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:32[0m 1s/step
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m57/89[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 900us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 963us/step
Global accuracy score (validation) = 57.27 [%]
Global F1 score (validation) = 56.96 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.33518466 0.26167798 0.3067999  0.09633738]
 [0.0587173  0.06663384 0.86948043 0.0051684 ]
 [0.18688005 0.14769977 0.6013783  0.06404188]
 ...
 [0.22905725 0.18069983 0.49980938 0.09043353]
 [0.27110955 0.18787076 0.40041158 0.14060809]
 [0.2547512  0.16939652 0.5056037  0.07024863]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 57.03 [%]
Global accuracy score (test) = 49.45 [%]
Global F1 score (train) = 57.45 [%]
Global F1 score (test) = 49.61 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.36      0.32      0.34       400
MODERATE-INTENSITY       0.46      0.52      0.49       400
         SEDENTARY       0.50      0.66      0.57       400
VIGOROUS-INTENSITY       0.75      0.48      0.59       345

          accuracy                           0.49      1545
         macro avg       0.52      0.49      0.50      1545
      weighted avg       0.51      0.49      0.49      1545

2025-11-05 10:43:48.412014: 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`.
2025-11-05 10:43:48.423490: 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 00:00:1762335828.436578 3017076 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762335828.440687 3017076 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762335828.450532 3017076 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335828.450550 3017076 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335828.450552 3017076 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335828.450553 3017076 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:43:48.453790: 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 00:00:1762335830.677373 3017076 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 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..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/53
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762335832.280965 3017209 service.cc:152] XLA service 0x7cd7dc01d7a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762335832.281022 3017209 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:43:52.314097: 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 00:00:1762335832.480398 3017209 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762335834.669586 3017209 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18:19[0m 3s/step - accuracy: 0.3125 - loss: 2.8221
[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2955 - loss: 2.4714  
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2915 - loss: 2.3428
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2907 - loss: 2.2563
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2927 - loss: 2.1813
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2944 - loss: 2.1181
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2960 - loss: 2.0641
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2975 - loss: 2.0143
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2986 - loss: 1.9755
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step - accuracy: 0.2990 - loss: 1.9615
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 10ms/step - accuracy: 0.2990 - loss: 1.9606 - val_accuracy: 0.4684 - val_loss: 1.2567
Epoch 2/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3125 - loss: 1.3989
[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3243 - loss: 1.4059 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3286 - loss: 1.3985
[1m109/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3315 - loss: 1.3941
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3335 - loss: 1.3898
[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3349 - loss: 1.3866
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[1m258/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3380 - loss: 1.3803
[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3394 - loss: 1.3775
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Epoch 3/53

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[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3951 - loss: 1.3092
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[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3893 - loss: 1.3098
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[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3877 - loss: 1.3084
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3871 - loss: 1.3082
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Epoch 4/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3898 - loss: 1.2921 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3903 - loss: 1.2863
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3908 - loss: 1.2832
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3912 - loss: 1.2821
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[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3933 - loss: 1.2791
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3939 - loss: 1.2786
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3945 - loss: 1.2784
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3948 - loss: 1.2783 - val_accuracy: 0.4940 - val_loss: 1.2043
Epoch 5/53

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4150 - loss: 1.2836 
[1m 69/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4062 - loss: 1.2794
[1m110/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4052 - loss: 1.2773
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4066 - loss: 1.2756
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4074 - loss: 1.2741
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4082 - loss: 1.2723
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4089 - loss: 1.2707
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4092 - loss: 1.2693
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4095 - loss: 1.2685 - val_accuracy: 0.5112 - val_loss: 1.1900
Epoch 6/53

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[1m 42/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4027 - loss: 1.2719 
[1m 82/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4047 - loss: 1.2644
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4075 - loss: 1.2606
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4098 - loss: 1.2581
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4117 - loss: 1.2558
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4131 - loss: 1.2540
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4143 - loss: 1.2526
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4148 - loss: 1.2517
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4150 - loss: 1.2514 - val_accuracy: 0.5235 - val_loss: 1.1688
Epoch 7/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4470 - loss: 1.2177 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4454 - loss: 1.2156
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4448 - loss: 1.2145
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4431 - loss: 1.2164
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4411 - loss: 1.2178
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4394 - loss: 1.2189
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4383 - loss: 1.2196
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Epoch 8/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3992 - loss: 1.2444 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4194 - loss: 1.2336
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4276 - loss: 1.2282
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4325 - loss: 1.2240
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[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4360 - loss: 1.2210
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4366 - loss: 1.2208
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4372 - loss: 1.2205
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4374 - loss: 1.2202 - val_accuracy: 0.5151 - val_loss: 1.1529
Epoch 9/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1446
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4610 - loss: 1.1701 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4597 - loss: 1.1790
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4581 - loss: 1.1808
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4571 - loss: 1.1824
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4560 - loss: 1.1844
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4551 - loss: 1.1855
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4544 - loss: 1.1865
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4538 - loss: 1.1875
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4535 - loss: 1.1880 - val_accuracy: 0.5305 - val_loss: 1.1578
Epoch 10/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.1010
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4736 - loss: 1.1690 
[1m 71/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4730 - loss: 1.1795
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4691 - loss: 1.1869
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4679 - loss: 1.1885
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4665 - loss: 1.1899
[1m223/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4658 - loss: 1.1906
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4655 - loss: 1.1908
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4652 - loss: 1.1906
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4652 - loss: 1.1904 - val_accuracy: 0.5235 - val_loss: 1.1488
Epoch 11/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0598
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4736 - loss: 1.1443 
[1m 82/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4656 - loss: 1.1553
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4646 - loss: 1.1606
[1m165/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4644 - loss: 1.1640
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[1m244/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4650 - loss: 1.1688
[1m283/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4650 - loss: 1.1704
[1m321/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4646 - loss: 1.1720
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4646 - loss: 1.1723 - val_accuracy: 0.5144 - val_loss: 1.1404
Epoch 12/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4539 - loss: 1.1876 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4614 - loss: 1.1838
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4671 - loss: 1.1791
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4697 - loss: 1.1769
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4705 - loss: 1.1759
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4706 - loss: 1.1757
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4708 - loss: 1.1751
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4710 - loss: 1.1747
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4711 - loss: 1.1745 - val_accuracy: 0.5344 - val_loss: 1.1404
Epoch 13/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4688 - loss: 1.1192
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4798 - loss: 1.1599 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4753 - loss: 1.1649
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4740 - loss: 1.1673
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4742 - loss: 1.1675
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4743 - loss: 1.1672
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4740 - loss: 1.1675
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4735 - loss: 1.1683
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4731 - loss: 1.1690
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4728 - loss: 1.1694 - val_accuracy: 0.5263 - val_loss: 1.1503
Epoch 14/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4793 - loss: 1.1781 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4823 - loss: 1.1759
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4819 - loss: 1.1735
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4809 - loss: 1.1726
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4800 - loss: 1.1711
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4797 - loss: 1.1697
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4795 - loss: 1.1690
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4796 - loss: 1.1681
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4797 - loss: 1.1677 - val_accuracy: 0.5386 - val_loss: 1.1390
Epoch 15/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5000 - loss: 1.0618
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4745 - loss: 1.1261 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4690 - loss: 1.1421
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4681 - loss: 1.1501
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4673 - loss: 1.1542
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4677 - loss: 1.1559
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4685 - loss: 1.1572
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4698 - loss: 1.1575
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4710 - loss: 1.1573
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4713 - loss: 1.1572 - val_accuracy: 0.5351 - val_loss: 1.1320
Epoch 16/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3750 - loss: 1.2078
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4885 - loss: 1.1360 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4914 - loss: 1.1360
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4882 - loss: 1.1393
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4862 - loss: 1.1414
[1m200/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4856 - loss: 1.1422
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4856 - loss: 1.1426
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4854 - loss: 1.1434
[1m319/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4850 - loss: 1.1444
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4849 - loss: 1.1447 - val_accuracy: 0.5418 - val_loss: 1.1441
Epoch 17/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4916 - loss: 1.1274 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4783 - loss: 1.1415
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4789 - loss: 1.1403
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4798 - loss: 1.1396
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4809 - loss: 1.1397
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4817 - loss: 1.1400
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4823 - loss: 1.1402
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4825 - loss: 1.1406
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4825 - loss: 1.1407 - val_accuracy: 0.5393 - val_loss: 1.1171
Epoch 18/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.3125 - loss: 1.3436
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4660 - loss: 1.1494 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4753 - loss: 1.1490
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4792 - loss: 1.1476
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4806 - loss: 1.1458
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4825 - loss: 1.1436
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4841 - loss: 1.1423
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4852 - loss: 1.1416
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4858 - loss: 1.1413
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4860 - loss: 1.1412 - val_accuracy: 0.5474 - val_loss: 1.1247
Epoch 19/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5190 - loss: 1.1156 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5130 - loss: 1.1150
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5114 - loss: 1.1136
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5092 - loss: 1.1146
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5064 - loss: 1.1178
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5044 - loss: 1.1206
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5026 - loss: 1.1229
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5012 - loss: 1.1246
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5006 - loss: 1.1253 - val_accuracy: 0.5379 - val_loss: 1.1329
Epoch 20/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5000 - loss: 1.1364
[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4969 - loss: 1.1516 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4855 - loss: 1.1461
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4865 - loss: 1.1422
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4883 - loss: 1.1395
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4892 - loss: 1.1380
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4897 - loss: 1.1372
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4904 - loss: 1.1362
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4909 - loss: 1.1356
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4912 - loss: 1.1354 - val_accuracy: 0.5639 - val_loss: 1.1214
Epoch 21/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3750 - loss: 1.2554
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5052 - loss: 1.0968 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5018 - loss: 1.1077
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4999 - loss: 1.1133
[1m162/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4997 - loss: 1.1160
[1m202/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4998 - loss: 1.1175
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4996 - loss: 1.1188
[1m281/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4990 - loss: 1.1204
[1m319/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4984 - loss: 1.1214
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4982 - loss: 1.1217 - val_accuracy: 0.5618 - val_loss: 1.1181
Epoch 22/53

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[1m 42/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5051 - loss: 1.1382 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5004 - loss: 1.1279
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4973 - loss: 1.1252
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4959 - loss: 1.1253
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4970 - loss: 1.1241
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4974 - loss: 1.1235
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4978 - loss: 1.1234
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4980 - loss: 1.1234
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4980 - loss: 1.1233 - val_accuracy: 0.5586 - val_loss: 1.1079
Epoch 23/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.2500 - loss: 1.5326
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4528 - loss: 1.2011 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4720 - loss: 1.1731
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4792 - loss: 1.1604
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4836 - loss: 1.1525
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4875 - loss: 1.1456
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4901 - loss: 1.1412
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4922 - loss: 1.1374
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4940 - loss: 1.1343
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4949 - loss: 1.1329 - val_accuracy: 0.5534 - val_loss: 1.1048
Epoch 24/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1252
[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4755 - loss: 1.1419 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4866 - loss: 1.1392
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4931 - loss: 1.1334
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4963 - loss: 1.1286
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4994 - loss: 1.1243
[1m223/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5009 - loss: 1.1222
[1m263/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5017 - loss: 1.1210
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5024 - loss: 1.1202
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5028 - loss: 1.1195 - val_accuracy: 0.5674 - val_loss: 1.1063
Epoch 25/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.6250 - loss: 0.9793
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5018 - loss: 1.1113 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5088 - loss: 1.1042
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5116 - loss: 1.1011
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5127 - loss: 1.1011
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5132 - loss: 1.1013
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5133 - loss: 1.1017
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5128 - loss: 1.1023
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5123 - loss: 1.1029
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5120 - loss: 1.1031 - val_accuracy: 0.5772 - val_loss: 1.0995
Epoch 26/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5000 - loss: 1.1439
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4974 - loss: 1.1337 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4980 - loss: 1.1178
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5002 - loss: 1.1131
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5013 - loss: 1.1111
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5019 - loss: 1.1098
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5028 - loss: 1.1092
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5038 - loss: 1.1087
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5045 - loss: 1.1082
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5048 - loss: 1.1081 - val_accuracy: 0.5650 - val_loss: 1.0987
Epoch 27/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5231 - loss: 1.1132 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5160 - loss: 1.1123
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5146 - loss: 1.1080
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5145 - loss: 1.1058
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[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5144 - loss: 1.1029
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5147 - loss: 1.1016
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5148 - loss: 1.1012 - val_accuracy: 0.5772 - val_loss: 1.0994
Epoch 28/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.2090
[1m 43/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5236 - loss: 1.1195 
[1m 86/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5248 - loss: 1.1078
[1m126/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5256 - loss: 1.1019
[1m162/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5263 - loss: 1.0999
[1m200/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5260 - loss: 1.0994
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5251 - loss: 1.0994
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5243 - loss: 1.0991
[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5237 - loss: 1.0986
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5236 - loss: 1.0984 - val_accuracy: 0.5692 - val_loss: 1.0812
Epoch 29/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5219 - loss: 1.1034 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5288 - loss: 1.0866
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5287 - loss: 1.0821
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5275 - loss: 1.0808
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5267 - loss: 1.0809
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5265 - loss: 1.0812
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5263 - loss: 1.0818
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5259 - loss: 1.0826
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5257 - loss: 1.0830 - val_accuracy: 0.5723 - val_loss: 1.1038
Epoch 30/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6562 - loss: 0.9301
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5303 - loss: 1.0860 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5270 - loss: 1.0841
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5245 - loss: 1.0843
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5228 - loss: 1.0852
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5214 - loss: 1.0862
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5208 - loss: 1.0866
[1m281/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5206 - loss: 1.0870
[1m321/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5207 - loss: 1.0871
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5208 - loss: 1.0871 - val_accuracy: 0.5864 - val_loss: 1.0839
Epoch 31/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 0.9698
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5106 - loss: 1.0923 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5099 - loss: 1.0948
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5104 - loss: 1.0955
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5110 - loss: 1.0949
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5116 - loss: 1.0942
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5124 - loss: 1.0939
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5128 - loss: 1.0939
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5133 - loss: 1.0931
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5135 - loss: 1.0927 - val_accuracy: 0.5811 - val_loss: 1.1060
Epoch 32/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5519 - loss: 1.0773 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5436 - loss: 1.0798
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5365 - loss: 1.0831
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5333 - loss: 1.0843
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[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5292 - loss: 1.0836
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5280 - loss: 1.0836
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Epoch 33/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6562 - loss: 0.9186
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[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5434 - loss: 1.0564
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5402 - loss: 1.0582
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5389 - loss: 1.0591
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[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5376 - loss: 1.0634
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 375ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 17ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 17ms/step
Saved model to disk.

Accuracy capturado en la ejecución 8: 49.45 [%]
F1-score capturado en la ejecución 8: 49.61 [%]

=== EJECUCIÓN 9 ===

--- TRAIN (ejecución 9) ---

--- TEST (ejecución 9) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:29[0m 1s/step
[1m 67/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 764us/step
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 15ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m58/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 877us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 948us/step
Global accuracy score (validation) = 57.2 [%]
Global F1 score (validation) = 56.28 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.18050484 0.16447383 0.577289   0.07773237]
 [0.30020744 0.31256777 0.21573326 0.17149149]
 [0.25700366 0.19831741 0.50218827 0.04249061]
 ...
 [0.17307933 0.19190946 0.5625986  0.07241264]
 [0.21717465 0.13940755 0.5292851  0.11413284]
 [0.19986837 0.15873261 0.54907006 0.09232893]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 56.65 [%]
Global accuracy score (test) = 50.87 [%]
Global F1 score (train) = 56.51 [%]
Global F1 score (test) = 50.36 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.39      0.27      0.32       400
MODERATE-INTENSITY       0.46      0.59      0.52       400
         SEDENTARY       0.52      0.69      0.59       400
VIGOROUS-INTENSITY       0.75      0.48      0.59       345

          accuracy                           0.51      1545
         macro avg       0.53      0.51      0.50      1545
      weighted avg       0.52      0.51      0.50      1545

2025-11-05 10:44:27.499356: 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`.
2025-11-05 10:44:27.510693: 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 00:00:1762335867.523917 3021133 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762335867.528063 3021133 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762335867.538151 3021133 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335867.538171 3021133 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335867.538173 3021133 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335867.538174 3021133 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:44:27.541437: 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 00:00:1762335869.782625 3021133 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 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..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/53
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762335871.420110 3021230 service.cc:152] XLA service 0x7a96ec01d8c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762335871.420143 3021230 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:44:31.454292: 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 00:00:1762335871.633057 3021230 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762335873.818186 3021230 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 2/53

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Epoch 3/53

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Epoch 4/53

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[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3795 - loss: 1.2951
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Epoch 5/53

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[1m 83/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4019 - loss: 1.2672
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[1m162/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4056 - loss: 1.2693
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Epoch 6/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4222 - loss: 1.2806 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4188 - loss: 1.2713
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[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4160 - loss: 1.2638
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[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4160 - loss: 1.2602
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4158 - loss: 1.2591
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4157 - loss: 1.2583
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4157 - loss: 1.2580 - val_accuracy: 0.4937 - val_loss: 1.1809
Epoch 7/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4456 - loss: 1.2103 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4456 - loss: 1.2140
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4449 - loss: 1.2183
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4441 - loss: 1.2231
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4440 - loss: 1.2255
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4434 - loss: 1.2271
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4428 - loss: 1.2276
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4422 - loss: 1.2278
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4416 - loss: 1.2279 - val_accuracy: 0.5091 - val_loss: 1.1633
Epoch 8/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4170 - loss: 1.2285 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4246 - loss: 1.2301
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4290 - loss: 1.2300
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4325 - loss: 1.2294
[1m199/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4352 - loss: 1.2276
[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4371 - loss: 1.2254
[1m283/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4385 - loss: 1.2235
[1m323/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4397 - loss: 1.2218
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Epoch 9/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4355 - loss: 1.2511 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4303 - loss: 1.2432
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4318 - loss: 1.2345
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4329 - loss: 1.2293
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4343 - loss: 1.2245
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4358 - loss: 1.2206
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4370 - loss: 1.2180
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4379 - loss: 1.2158
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4384 - loss: 1.2147 - val_accuracy: 0.5214 - val_loss: 1.1576
Epoch 10/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3750 - loss: 1.2405
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4419 - loss: 1.1828 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4462 - loss: 1.1856
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4494 - loss: 1.1861
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4513 - loss: 1.1864
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[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4550 - loss: 1.1859
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4555 - loss: 1.1864
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4554 - loss: 1.1872
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4553 - loss: 1.1876 - val_accuracy: 0.5084 - val_loss: 1.1501
Epoch 11/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 1.1180
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4567 - loss: 1.1813 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4540 - loss: 1.1888
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4548 - loss: 1.1909
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4559 - loss: 1.1889
[1m199/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4564 - loss: 1.1875
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4569 - loss: 1.1870
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4577 - loss: 1.1863
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4580 - loss: 1.1860
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4581 - loss: 1.1860 - val_accuracy: 0.5400 - val_loss: 1.1513
Epoch 12/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4062 - loss: 1.2388
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4554 - loss: 1.1763 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4514 - loss: 1.1795
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4521 - loss: 1.1817
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4545 - loss: 1.1811
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4556 - loss: 1.1806
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4568 - loss: 1.1803
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4582 - loss: 1.1794
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4593 - loss: 1.1789
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4596 - loss: 1.1787 - val_accuracy: 0.5453 - val_loss: 1.1354
Epoch 13/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5938 - loss: 1.0645
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4634 - loss: 1.1608 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4623 - loss: 1.1723
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4615 - loss: 1.1734
[1m146/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4614 - loss: 1.1723
[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4618 - loss: 1.1713
[1m219/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4621 - loss: 1.1706
[1m256/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4624 - loss: 1.1701
[1m291/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4632 - loss: 1.1694
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4642 - loss: 1.1689 - val_accuracy: 0.5513 - val_loss: 1.1342
Epoch 14/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4859 - loss: 1.1550 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4784 - loss: 1.1565
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4766 - loss: 1.1580
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4771 - loss: 1.1565
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4777 - loss: 1.1554
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4779 - loss: 1.1554
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4782 - loss: 1.1552
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4782 - loss: 1.1551
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4782 - loss: 1.1550 - val_accuracy: 0.5344 - val_loss: 1.1267
Epoch 15/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4062 - loss: 1.1652
[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4756 - loss: 1.1561 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4808 - loss: 1.1523
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4846 - loss: 1.1493
[1m143/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4842 - loss: 1.1503
[1m178/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4833 - loss: 1.1512
[1m215/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4829 - loss: 1.1514
[1m254/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4826 - loss: 1.1515
[1m291/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4823 - loss: 1.1512
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4818 - loss: 1.1511
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4818 - loss: 1.1511 - val_accuracy: 0.5362 - val_loss: 1.1372
Epoch 16/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4359 - loss: 1.1691 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4472 - loss: 1.1637
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4533 - loss: 1.1615
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4561 - loss: 1.1601
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4588 - loss: 1.1577
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4612 - loss: 1.1558
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4630 - loss: 1.1547
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4643 - loss: 1.1540
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4650 - loss: 1.1538 - val_accuracy: 0.5449 - val_loss: 1.1286
Epoch 17/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.5000 - loss: 1.0913
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4420 - loss: 1.1673 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4473 - loss: 1.1676
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4545 - loss: 1.1653
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4597 - loss: 1.1625
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4640 - loss: 1.1587
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4667 - loss: 1.1562
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4683 - loss: 1.1547
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4697 - loss: 1.1535
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4703 - loss: 1.1528 - val_accuracy: 0.5309 - val_loss: 1.1358
Epoch 18/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5938 - loss: 0.8946
[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4775 - loss: 1.1243 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4724 - loss: 1.1356
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4740 - loss: 1.1391
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4740 - loss: 1.1411
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4755 - loss: 1.1409
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4770 - loss: 1.1401
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4780 - loss: 1.1400
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4784 - loss: 1.1403
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4785 - loss: 1.1403 - val_accuracy: 0.5456 - val_loss: 1.1237
Epoch 19/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.2426
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4869 - loss: 1.1664 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4949 - loss: 1.1486
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4977 - loss: 1.1423
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4984 - loss: 1.1392
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4993 - loss: 1.1368
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4996 - loss: 1.1349
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4994 - loss: 1.1341
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4990 - loss: 1.1338
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4988 - loss: 1.1336 - val_accuracy: 0.5485 - val_loss: 1.1135
Epoch 20/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1070
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4968 - loss: 1.1056 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4919 - loss: 1.1195
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4903 - loss: 1.1256
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4898 - loss: 1.1284
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4903 - loss: 1.1289
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4911 - loss: 1.1284
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4914 - loss: 1.1280
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4913 - loss: 1.1277
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4912 - loss: 1.1277 - val_accuracy: 0.5499 - val_loss: 1.1127
Epoch 21/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6250 - loss: 1.0067
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5137 - loss: 1.1206 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5105 - loss: 1.1181
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5050 - loss: 1.1203
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5012 - loss: 1.1226
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4990 - loss: 1.1238
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4977 - loss: 1.1247
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4968 - loss: 1.1253
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4960 - loss: 1.1261
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4959 - loss: 1.1262 - val_accuracy: 0.5435 - val_loss: 1.0994
Epoch 22/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4062 - loss: 1.2071
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5146 - loss: 1.1233 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5231 - loss: 1.1071
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5235 - loss: 1.1047
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5236 - loss: 1.1041
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5223 - loss: 1.1049
[1m226/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5200 - loss: 1.1064
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5179 - loss: 1.1081
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5161 - loss: 1.1095
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5150 - loss: 1.1102 - val_accuracy: 0.5650 - val_loss: 1.0954
Epoch 23/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5625 - loss: 1.0993
[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5153 - loss: 1.1169 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5097 - loss: 1.1155
[1m110/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5086 - loss: 1.1151
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5078 - loss: 1.1144
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5076 - loss: 1.1134
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5076 - loss: 1.1124
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5072 - loss: 1.1123
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5067 - loss: 1.1124
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5063 - loss: 1.1126 - val_accuracy: 0.5576 - val_loss: 1.1063
Epoch 24/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.3674
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4754 - loss: 1.1569 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4771 - loss: 1.1529
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4824 - loss: 1.1447
[1m161/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4845 - loss: 1.1413
[1m201/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4867 - loss: 1.1383
[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4886 - loss: 1.1356
[1m281/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4901 - loss: 1.1338
[1m321/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4914 - loss: 1.1321
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4916 - loss: 1.1318 - val_accuracy: 0.5530 - val_loss: 1.1080
Epoch 25/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1253
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5053 - loss: 1.1129 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4979 - loss: 1.1156
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4990 - loss: 1.1134
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5002 - loss: 1.1136
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5014 - loss: 1.1139
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5020 - loss: 1.1135
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5022 - loss: 1.1128
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5025 - loss: 1.1118
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5026 - loss: 1.1113 - val_accuracy: 0.5734 - val_loss: 1.1132
Epoch 26/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.2812 - loss: 1.2792
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4787 - loss: 1.1282 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4934 - loss: 1.1175
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4973 - loss: 1.1124
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4990 - loss: 1.1099
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5002 - loss: 1.1087
[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5013 - loss: 1.1077
[1m262/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5020 - loss: 1.1071
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5027 - loss: 1.1065
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5030 - loss: 1.1061 - val_accuracy: 0.5488 - val_loss: 1.1493
Epoch 27/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 1.3170
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5023 - loss: 1.1234 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4996 - loss: 1.1165
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5013 - loss: 1.1107
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5026 - loss: 1.1085
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5035 - loss: 1.1067
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5044 - loss: 1.1052
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5055 - loss: 1.1042
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5062 - loss: 1.1036
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5064 - loss: 1.1034 - val_accuracy: 0.5695 - val_loss: 1.0920
Epoch 28/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1175
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5193 - loss: 1.0893 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5217 - loss: 1.0803
[1m123/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5196 - loss: 1.0816
[1m161/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5191 - loss: 1.0833
[1m201/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5182 - loss: 1.0854
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5175 - loss: 1.0868
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5173 - loss: 1.0875
[1m319/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5171 - loss: 1.0883
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5171 - loss: 1.0885 - val_accuracy: 0.5646 - val_loss: 1.0902
Epoch 29/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0754
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5522 - loss: 1.0480 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5435 - loss: 1.0603
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5390 - loss: 1.0668
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5363 - loss: 1.0714
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5346 - loss: 1.0746
[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5328 - loss: 1.0767
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5309 - loss: 1.0786
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5294 - loss: 1.0801
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5283 - loss: 1.0812 - val_accuracy: 0.5797 - val_loss: 1.0929
Epoch 30/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4688 - loss: 0.9875
[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4956 - loss: 1.0875 
[1m 71/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4964 - loss: 1.1054
[1m110/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4995 - loss: 1.1059
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5015 - loss: 1.1043
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5037 - loss: 1.1015
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5060 - loss: 1.0989
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5074 - loss: 1.0977
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5084 - loss: 1.0965
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5088 - loss: 1.0960 - val_accuracy: 0.5600 - val_loss: 1.1006
Epoch 31/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1795
[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5185 - loss: 1.0789 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5165 - loss: 1.0828
[1m109/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5149 - loss: 1.0826
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5148 - loss: 1.0830
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5157 - loss: 1.0831
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5170 - loss: 1.0828
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5180 - loss: 1.0828
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5189 - loss: 1.0828
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5191 - loss: 1.0828 - val_accuracy: 0.5667 - val_loss: 1.0943
Epoch 32/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9533
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5315 - loss: 1.0708 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5265 - loss: 1.0795
[1m110/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5230 - loss: 1.0803
[1m143/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5220 - loss: 1.0797
[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5214 - loss: 1.0793
[1m217/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5213 - loss: 1.0786
[1m253/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5211 - loss: 1.0784
[1m289/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5210 - loss: 1.0783
[1m325/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5211 - loss: 1.0782
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5211 - loss: 1.0782 - val_accuracy: 0.5614 - val_loss: 1.0987
Epoch 33/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.0600
[1m 42/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4999 - loss: 1.0920 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5095 - loss: 1.0880
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5147 - loss: 1.0860
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5169 - loss: 1.0857
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5181 - loss: 1.0850
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5188 - loss: 1.0848
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 396ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 16ms/step  
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Saved model to disk.

Accuracy capturado en la ejecución 9: 50.87 [%]
F1-score capturado en la ejecución 9: 50.36 [%]

=== EJECUCIÓN 10 ===

--- TRAIN (ejecución 10) ---

--- TEST (ejecución 10) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:17[0m 971ms/step
[1m 67/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 767us/step  
[1m138/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 738us/step
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[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 746us/step
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m69/89[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 740us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 868us/step
Global accuracy score (validation) = 58.04 [%]
Global F1 score (validation) = 56.82 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.06885043 0.09075946 0.8340089  0.00638121]
 [0.30718082 0.27445805 0.23944347 0.17891768]
 [0.0735618  0.09467108 0.8236064  0.00816074]
 ...
 [0.23342557 0.16889487 0.4748605  0.12281903]
 [0.217642   0.14308718 0.52591544 0.11335535]
 [0.19854353 0.13511837 0.6001014  0.06623661]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 56.4 [%]
Global accuracy score (test) = 52.17 [%]
Global F1 score (train) = 56.32 [%]
Global F1 score (test) = 51.64 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.40      0.29      0.34       400
MODERATE-INTENSITY       0.51      0.60      0.55       400
         SEDENTARY       0.51      0.69      0.58       400
VIGOROUS-INTENSITY       0.73      0.50      0.59       345

          accuracy                           0.52      1545
         macro avg       0.54      0.52      0.52      1545
      weighted avg       0.53      0.52      0.51      1545

2025-11-05 10:45:06.946447: 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`.
2025-11-05 10:45:06.957766: 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 00:00:1762335906.970883 3025180 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762335906.975152 3025180 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762335906.985320 3025180 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335906.985348 3025180 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335906.985350 3025180 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335906.985351 3025180 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:45:06.988416: 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 00:00:1762335909.227986 3025180 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 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..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/53
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762335910.826949 3025292 service.cc:152] XLA service 0x736850013440 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762335910.826988 3025292 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:45:10.860950: 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 00:00:1762335911.033763 3025292 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762335913.199669 3025292 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 2/53

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Epoch 3/53

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[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3689 - loss: 1.3210
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3707 - loss: 1.3192
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Epoch 4/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3978 - loss: 1.2605 
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[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4004 - loss: 1.2614
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4010 - loss: 1.2627
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4014 - loss: 1.2637
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4016 - loss: 1.2641
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4018 - loss: 1.2645
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4023 - loss: 1.2647
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4026 - loss: 1.2647 - val_accuracy: 0.4814 - val_loss: 1.1935
Epoch 5/53

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[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3839 - loss: 1.2876 
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4146 - loss: 1.2606 - val_accuracy: 0.5035 - val_loss: 1.1800
Epoch 6/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3996 - loss: 1.2650 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4036 - loss: 1.2554
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4098 - loss: 1.2493
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4138 - loss: 1.2467
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4168 - loss: 1.2455
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4192 - loss: 1.2440
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4210 - loss: 1.2421
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4227 - loss: 1.2404
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4233 - loss: 1.2397 - val_accuracy: 0.5053 - val_loss: 1.1801
Epoch 7/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4447 - loss: 1.2104 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4512 - loss: 1.2070
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4522 - loss: 1.2050
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4527 - loss: 1.2046
[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4527 - loss: 1.2049
[1m223/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4519 - loss: 1.2061
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4512 - loss: 1.2070
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4505 - loss: 1.2074
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4500 - loss: 1.2076 - val_accuracy: 0.4730 - val_loss: 1.1830
Epoch 8/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4465 - loss: 1.1891 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4462 - loss: 1.1926
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4476 - loss: 1.1933
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4493 - loss: 1.1917
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4499 - loss: 1.1917
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4497 - loss: 1.1925
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4493 - loss: 1.1933
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4490 - loss: 1.1936
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4490 - loss: 1.1937 - val_accuracy: 0.5119 - val_loss: 1.1519
Epoch 9/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.5312 - loss: 1.0374
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4538 - loss: 1.1973 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4441 - loss: 1.2031
[1m110/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4422 - loss: 1.2026
[1m146/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4410 - loss: 1.2012
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4417 - loss: 1.1989
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4430 - loss: 1.1974
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4440 - loss: 1.1963
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4450 - loss: 1.1954
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4455 - loss: 1.1952 - val_accuracy: 0.5070 - val_loss: 1.1570
Epoch 10/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1483
[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4614 - loss: 1.1656 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4528 - loss: 1.1808
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4504 - loss: 1.1845
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4476 - loss: 1.1870
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4467 - loss: 1.1879
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[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4463 - loss: 1.1886
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4466 - loss: 1.1887
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4468 - loss: 1.1888 - val_accuracy: 0.5102 - val_loss: 1.1513
Epoch 11/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.2358
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4639 - loss: 1.1995 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4715 - loss: 1.1869
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4722 - loss: 1.1800
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4718 - loss: 1.1766
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[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4702 - loss: 1.1736
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4700 - loss: 1.1732
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4698 - loss: 1.1731
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4697 - loss: 1.1730 - val_accuracy: 0.5172 - val_loss: 1.1564
Epoch 12/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4333 - loss: 1.2146 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4391 - loss: 1.2043
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4431 - loss: 1.1984
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4450 - loss: 1.1940
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4469 - loss: 1.1906
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4480 - loss: 1.1880
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4493 - loss: 1.1855
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4505 - loss: 1.1836
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4511 - loss: 1.1828 - val_accuracy: 0.5200 - val_loss: 1.1485
Epoch 13/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1649
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4719 - loss: 1.1547 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4664 - loss: 1.1676
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4635 - loss: 1.1732
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4634 - loss: 1.1735
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4635 - loss: 1.1731
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4639 - loss: 1.1722
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4642 - loss: 1.1717
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4645 - loss: 1.1711
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4647 - loss: 1.1706 - val_accuracy: 0.5320 - val_loss: 1.1391
Epoch 14/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.2474
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4855 - loss: 1.1255 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4893 - loss: 1.1266
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4907 - loss: 1.1279
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4904 - loss: 1.1298
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4894 - loss: 1.1319
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4879 - loss: 1.1344
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4864 - loss: 1.1365
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4850 - loss: 1.1385
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4844 - loss: 1.1394 - val_accuracy: 0.5088 - val_loss: 1.1298
Epoch 15/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4062 - loss: 1.1391
[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4294 - loss: 1.1934 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4445 - loss: 1.1802
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4519 - loss: 1.1752
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4556 - loss: 1.1717
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[1m261/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4605 - loss: 1.1659
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4615 - loss: 1.1644
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4623 - loss: 1.1635 - val_accuracy: 0.5274 - val_loss: 1.1279
Epoch 16/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4664 - loss: 1.1602 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4700 - loss: 1.1547
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4713 - loss: 1.1543
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4726 - loss: 1.1538
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[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4731 - loss: 1.1543
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4730 - loss: 1.1539
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4730 - loss: 1.1537
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4733 - loss: 1.1532 - val_accuracy: 0.5228 - val_loss: 1.1250
Epoch 17/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 1.1485
[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4880 - loss: 1.1658 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4890 - loss: 1.1569
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4861 - loss: 1.1547
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4849 - loss: 1.1526
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[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4836 - loss: 1.1481
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4831 - loss: 1.1466
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4829 - loss: 1.1452
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4828 - loss: 1.1446 - val_accuracy: 0.5200 - val_loss: 1.1102
Epoch 18/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.6250 - loss: 0.9919
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5355 - loss: 1.0898 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5168 - loss: 1.1122
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5080 - loss: 1.1204
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5031 - loss: 1.1248
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5005 - loss: 1.1271
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4991 - loss: 1.1283
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4979 - loss: 1.1287
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4971 - loss: 1.1287
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4966 - loss: 1.1289 - val_accuracy: 0.5372 - val_loss: 1.1471
Epoch 19/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 0.9797
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4902 - loss: 1.1000 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4890 - loss: 1.1095
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4868 - loss: 1.1156
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4860 - loss: 1.1193
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4854 - loss: 1.1214
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4848 - loss: 1.1230
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4842 - loss: 1.1245
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4838 - loss: 1.1259
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4838 - loss: 1.1263 - val_accuracy: 0.5239 - val_loss: 1.1281
Epoch 20/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 1.1162
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5116 - loss: 1.1005 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5083 - loss: 1.1072
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5057 - loss: 1.1099
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5045 - loss: 1.1120
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5035 - loss: 1.1132
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5022 - loss: 1.1145
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5013 - loss: 1.1156
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5006 - loss: 1.1164
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5002 - loss: 1.1168 - val_accuracy: 0.5130 - val_loss: 1.1214
Epoch 21/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.0677
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4664 - loss: 1.1416 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4791 - loss: 1.1234
[1m105/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4840 - loss: 1.1173
[1m142/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4868 - loss: 1.1152
[1m179/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4872 - loss: 1.1155
[1m218/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4874 - loss: 1.1161
[1m259/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4883 - loss: 1.1166
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4887 - loss: 1.1168
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4890 - loss: 1.1169 - val_accuracy: 0.5400 - val_loss: 1.1438
Epoch 22/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1259
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4998 - loss: 1.1100 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4988 - loss: 1.1137
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4973 - loss: 1.1152
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4966 - loss: 1.1154
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4969 - loss: 1.1144
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4973 - loss: 1.1132
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4972 - loss: 1.1132
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4970 - loss: 1.1137
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4968 - loss: 1.1139 - val_accuracy: 0.5502 - val_loss: 1.1146

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 395ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 16ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 16ms/step
Saved model to disk.

Accuracy capturado en la ejecución 10: 52.17 [%]
F1-score capturado en la ejecución 10: 51.64 [%]

=== EJECUCIÓN 11 ===

--- TRAIN (ejecución 11) ---

--- TEST (ejecución 11) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:27[0m 1s/step
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 820us/step
[1m133/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 758us/step
[1m200/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 754us/step
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 738us/step
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 15ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m66/89[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 778us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 898us/step
Global accuracy score (validation) = 55.23 [%]
Global F1 score (validation) = 54.92 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.32514194 0.39197078 0.13302721 0.14986008]
 [0.31344846 0.38041455 0.14410509 0.16203183]
 [0.32514194 0.39197078 0.13302721 0.14986008]
 ...
 [0.23489933 0.15898849 0.4961035  0.11000865]
 [0.22731344 0.13129658 0.47944513 0.16194484]
 [0.209669   0.14141281 0.59880996 0.05010828]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 52.13 [%]
Global accuracy score (test) = 48.28 [%]
Global F1 score (train) = 52.82 [%]
Global F1 score (test) = 48.86 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.36      0.35      0.35       400
MODERATE-INTENSITY       0.41      0.43      0.42       400
         SEDENTARY       0.51      0.65      0.57       400
VIGOROUS-INTENSITY       0.76      0.51      0.61       345

          accuracy                           0.48      1545
         macro avg       0.51      0.48      0.49      1545
      weighted avg       0.50      0.48      0.48      1545

2025-11-05 10:45:39.707307: 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`.
2025-11-05 10:45:39.718495: 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 00:00:1762335939.731681 3028166 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762335939.735854 3028166 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762335939.746660 3028166 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335939.746685 3028166 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335939.746687 3028166 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335939.746689 3028166 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:45:39.750051: 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 00:00:1762335941.974769 3028166 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 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..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/53
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762335943.567060 3028291 service.cc:152] XLA service 0x78d60c00c470 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762335943.567109 3028291 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:45:43.605876: 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 00:00:1762335943.773198 3028291 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762335945.929082 3028291 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18:13[0m 3s/step - accuracy: 0.2500 - loss: 2.7780
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[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2766 - loss: 2.1043
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2792 - loss: 2.0596
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Epoch 2/53

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[1m257/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3276 - loss: 1.3758
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Epoch 3/53

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Epoch 4/53

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[1m161/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4047 - loss: 1.2855
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[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4019 - loss: 1.2868
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Epoch 5/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3967 - loss: 1.2736 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4027 - loss: 1.2720
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4039 - loss: 1.2713
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4056 - loss: 1.2693
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[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4080 - loss: 1.2661
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4087 - loss: 1.2650
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4091 - loss: 1.2643
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4094 - loss: 1.2638 - val_accuracy: 0.4867 - val_loss: 1.1785
Epoch 6/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4517 - loss: 1.2379 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4393 - loss: 1.2394
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4376 - loss: 1.2377
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4367 - loss: 1.2365
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[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4336 - loss: 1.2369
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4328 - loss: 1.2369
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4321 - loss: 1.2371
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4317 - loss: 1.2372 - val_accuracy: 0.4856 - val_loss: 1.1774
Epoch 7/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4005 - loss: 1.2657 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4132 - loss: 1.2467
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4179 - loss: 1.2402
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4217 - loss: 1.2365
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4238 - loss: 1.2347
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4252 - loss: 1.2333
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4268 - loss: 1.2319
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4278 - loss: 1.2306
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4281 - loss: 1.2302 - val_accuracy: 0.5158 - val_loss: 1.1595
Epoch 8/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4192 - loss: 1.2149 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4250 - loss: 1.2151
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[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4262 - loss: 1.2219
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[1m263/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4296 - loss: 1.2216
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4310 - loss: 1.2210
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4319 - loss: 1.2205 - val_accuracy: 0.5144 - val_loss: 1.1571
Epoch 9/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4537 - loss: 1.1873 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4491 - loss: 1.1957
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4495 - loss: 1.1978
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4503 - loss: 1.1986
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4514 - loss: 1.1989
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[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4525 - loss: 1.1993
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4529 - loss: 1.1991
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4530 - loss: 1.1991 - val_accuracy: 0.5091 - val_loss: 1.1576
Epoch 10/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4546 - loss: 1.2108 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4495 - loss: 1.2077
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4483 - loss: 1.2076
[1m162/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4483 - loss: 1.2063
[1m199/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4490 - loss: 1.2047
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4499 - loss: 1.2029
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4508 - loss: 1.2014
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4513 - loss: 1.2003
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4516 - loss: 1.1999 - val_accuracy: 0.5091 - val_loss: 1.1441
Epoch 11/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4062 - loss: 1.1875
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4900 - loss: 1.1489 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4801 - loss: 1.1597
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4753 - loss: 1.1635
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4729 - loss: 1.1670
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4709 - loss: 1.1696
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4697 - loss: 1.1708
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4693 - loss: 1.1712
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4688 - loss: 1.1717
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4686 - loss: 1.1718 - val_accuracy: 0.5365 - val_loss: 1.1325
Epoch 12/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.2125
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4567 - loss: 1.1871 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4598 - loss: 1.1842
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4613 - loss: 1.1830
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4619 - loss: 1.1821
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4618 - loss: 1.1816
[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4609 - loss: 1.1810
[1m262/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4602 - loss: 1.1809
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4597 - loss: 1.1807
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4596 - loss: 1.1804 - val_accuracy: 0.4958 - val_loss: 1.1444
Epoch 13/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.2786
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4913 - loss: 1.1506 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4876 - loss: 1.1592
[1m109/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4861 - loss: 1.1616
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4831 - loss: 1.1636
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4815 - loss: 1.1652
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[1m262/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4796 - loss: 1.1657
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4785 - loss: 1.1659
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Epoch 14/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4504 - loss: 1.1372 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4543 - loss: 1.1416
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4552 - loss: 1.1477
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4561 - loss: 1.1507
[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4571 - loss: 1.1519
[1m223/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4578 - loss: 1.1536
[1m259/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4587 - loss: 1.1547
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4609 - loss: 1.1555 - val_accuracy: 0.5316 - val_loss: 1.1214
Epoch 15/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4869 - loss: 1.1399 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4839 - loss: 1.1384
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[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4840 - loss: 1.1392
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4832 - loss: 1.1416
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4819 - loss: 1.1439
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4811 - loss: 1.1455
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4803 - loss: 1.1469
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4801 - loss: 1.1472 - val_accuracy: 0.5239 - val_loss: 1.1158
Epoch 16/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4375 - loss: 1.0796
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4676 - loss: 1.1446 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4713 - loss: 1.1427
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4728 - loss: 1.1406
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4742 - loss: 1.1391
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4747 - loss: 1.1393
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4745 - loss: 1.1402
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4748 - loss: 1.1404
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4747 - loss: 1.1408
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4748 - loss: 1.1411 - val_accuracy: 0.5351 - val_loss: 1.1313
Epoch 17/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3750 - loss: 1.2712
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4688 - loss: 1.1448 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4720 - loss: 1.1422
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4761 - loss: 1.1400
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4785 - loss: 1.1389
[1m199/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4790 - loss: 1.1392
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4795 - loss: 1.1388
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4798 - loss: 1.1387
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4797 - loss: 1.1392
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4796 - loss: 1.1394 - val_accuracy: 0.5425 - val_loss: 1.1194
Epoch 18/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9685
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5164 - loss: 1.0795 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5077 - loss: 1.0998
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[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5018 - loss: 1.1146
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[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4979 - loss: 1.1219
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4959 - loss: 1.1251
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Epoch 19/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3438 - loss: 1.0793
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[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4820 - loss: 1.1263
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[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4839 - loss: 1.1276
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4844 - loss: 1.1277
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Epoch 20/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6250 - loss: 0.9522
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 410ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 16ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 16ms/step
Saved model to disk.

Accuracy capturado en la ejecución 11: 48.28 [%]
F1-score capturado en la ejecución 11: 48.86 [%]

=== EJECUCIÓN 12 ===

--- TRAIN (ejecución 12) ---

--- TEST (ejecución 12) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
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[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 823us/step
Global accuracy score (validation) = 55.9 [%]
Global F1 score (validation) = 54.15 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.2716046  0.22724102 0.41979924 0.08135521]
 [0.26191914 0.21932767 0.44056913 0.07818411]
 [0.3139155  0.3805433  0.13929571 0.16624552]
 ...
 [0.175015   0.12199467 0.6190301  0.08396021]
 [0.16813801 0.10051014 0.5910399  0.14031194]
 [0.23045182 0.1592545  0.52464956 0.08564415]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 52.69 [%]
Global accuracy score (test) = 51.46 [%]
Global F1 score (train) = 51.92 [%]
Global F1 score (test) = 50.41 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.41      0.23      0.29       400
MODERATE-INTENSITY       0.46      0.61      0.53       400
         SEDENTARY       0.51      0.70      0.59       400
VIGOROUS-INTENSITY       0.73      0.52      0.61       345

          accuracy                           0.51      1545
         macro avg       0.53      0.51      0.50      1545
      weighted avg       0.52      0.51      0.50      1545

2025-11-05 10:46:11.141913: 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`.
2025-11-05 10:46:11.153408: 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 00:00:1762335971.166407 3030975 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762335971.170580 3030975 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762335971.180227 3030975 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335971.180244 3030975 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335971.180246 3030975 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762335971.180247 3030975 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:46:11.183414: 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 00:00:1762335973.408175 3030975 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 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..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/53
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762335975.017559 3031108 service.cc:152] XLA service 0x70476c002830 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762335975.017593 3031108 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:46:15.052092: 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 00:00:1762335975.230323 3031108 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762335977.374692 3031108 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18:11[0m 3s/step - accuracy: 0.2500 - loss: 2.3193
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2512 - loss: 2.6031  
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2578 - loss: 2.4458
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[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2731 - loss: 2.2257
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2774 - loss: 2.1534
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2805 - loss: 2.0917
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2829 - loss: 2.0457
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2855 - loss: 2.0033
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step - accuracy: 0.2867 - loss: 1.9837
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 10ms/step - accuracy: 0.2868 - loss: 1.9827 - val_accuracy: 0.4589 - val_loss: 1.2553
Epoch 2/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3426 - loss: 1.4156 
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[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3405 - loss: 1.3978
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3407 - loss: 1.3928
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3420 - loss: 1.3884
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3432 - loss: 1.3848
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3448 - loss: 1.3813
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3463 - loss: 1.3778
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3469 - loss: 1.3764 - val_accuracy: 0.4702 - val_loss: 1.2298
Epoch 3/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3598 - loss: 1.3329 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3619 - loss: 1.3328
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[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3681 - loss: 1.3228
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3697 - loss: 1.3202
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Epoch 4/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3702 - loss: 1.2950 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3835 - loss: 1.2890
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Epoch 5/53

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[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4130 - loss: 1.2717
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[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4193 - loss: 1.2639
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[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4227 - loss: 1.2602
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4235 - loss: 1.2587
[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4241 - loss: 1.2576
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4242 - loss: 1.2573 - val_accuracy: 0.5011 - val_loss: 1.1766
Epoch 6/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4546 - loss: 1.2122 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4524 - loss: 1.2206
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4503 - loss: 1.2238
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4473 - loss: 1.2273
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4445 - loss: 1.2297
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4421 - loss: 1.2315
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4405 - loss: 1.2325
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4394 - loss: 1.2331
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4388 - loss: 1.2332 - val_accuracy: 0.4909 - val_loss: 1.1696
Epoch 7/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2812 - loss: 1.2876
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4434 - loss: 1.1970 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4427 - loss: 1.2038
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4413 - loss: 1.2097
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4404 - loss: 1.2136
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4401 - loss: 1.2153
[1m223/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4402 - loss: 1.2165
[1m259/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4401 - loss: 1.2172
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4401 - loss: 1.2176
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Epoch 8/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4307 - loss: 1.2236 
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[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4326 - loss: 1.2088
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4342 - loss: 1.2075
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4361 - loss: 1.2065
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4377 - loss: 1.2058
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4388 - loss: 1.2052
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4393 - loss: 1.2051 - val_accuracy: 0.5162 - val_loss: 1.1533
Epoch 9/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4489 - loss: 1.1785 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4521 - loss: 1.1836
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4509 - loss: 1.1891
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4502 - loss: 1.1921
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[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4501 - loss: 1.1952
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4505 - loss: 1.1952
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4507 - loss: 1.1951 - val_accuracy: 0.5337 - val_loss: 1.1421
Epoch 10/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 1.2006
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4634 - loss: 1.1670 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4653 - loss: 1.1691
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4653 - loss: 1.1722
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4653 - loss: 1.1747
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4645 - loss: 1.1773
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4640 - loss: 1.1785
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4634 - loss: 1.1796
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4632 - loss: 1.1803
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4631 - loss: 1.1807 - val_accuracy: 0.5249 - val_loss: 1.1411
Epoch 11/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.2771
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5033 - loss: 1.1628 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4859 - loss: 1.1715
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4781 - loss: 1.1734
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4754 - loss: 1.1739
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4734 - loss: 1.1746
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4716 - loss: 1.1750
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4707 - loss: 1.1752
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4697 - loss: 1.1758
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4692 - loss: 1.1762 - val_accuracy: 0.5298 - val_loss: 1.1368
Epoch 12/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1638
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4320 - loss: 1.1898 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4465 - loss: 1.1717
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4535 - loss: 1.1672
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4585 - loss: 1.1652
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4615 - loss: 1.1641
[1m226/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4635 - loss: 1.1635
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4649 - loss: 1.1633
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4662 - loss: 1.1631
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4668 - loss: 1.1632 - val_accuracy: 0.5369 - val_loss: 1.1412
Epoch 13/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 19ms/step - accuracy: 0.4688 - loss: 1.1974
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4861 - loss: 1.1570 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4867 - loss: 1.1575
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[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4855 - loss: 1.1578
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4832 - loss: 1.1597
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4815 - loss: 1.1604
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4807 - loss: 1.1604
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4796 - loss: 1.1608
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Epoch 14/53

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[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4665 - loss: 1.1697
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[1m163/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4706 - loss: 1.1630
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[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4738 - loss: 1.1599
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Epoch 15/53

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[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4729 - loss: 1.1576
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[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4743 - loss: 1.1538
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[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4744 - loss: 1.1516
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4743 - loss: 1.1508
[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4745 - loss: 1.1504
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4746 - loss: 1.1504 - val_accuracy: 0.5298 - val_loss: 1.1292
Epoch 16/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.1481
[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4732 - loss: 1.1140 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4811 - loss: 1.1188
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4807 - loss: 1.1234
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4800 - loss: 1.1268
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4803 - loss: 1.1292
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4803 - loss: 1.1310
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4806 - loss: 1.1325
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4806 - loss: 1.1339
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4806 - loss: 1.1346 - val_accuracy: 0.5428 - val_loss: 1.1227
Epoch 17/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5938 - loss: 1.0665
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5028 - loss: 1.1262 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4932 - loss: 1.1319
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4915 - loss: 1.1334
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4905 - loss: 1.1344
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4901 - loss: 1.1349
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4898 - loss: 1.1359
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4895 - loss: 1.1363
[1m317/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4889 - loss: 1.1368
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4887 - loss: 1.1370 - val_accuracy: 0.5334 - val_loss: 1.1278
Epoch 18/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6250 - loss: 1.0358
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5248 - loss: 1.0911 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5134 - loss: 1.1097
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5069 - loss: 1.1180
[1m161/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5034 - loss: 1.1226
[1m200/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5012 - loss: 1.1257
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4996 - loss: 1.1281
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4982 - loss: 1.1301
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4972 - loss: 1.1317
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4969 - loss: 1.1321 - val_accuracy: 0.5393 - val_loss: 1.1160
Epoch 19/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0705
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4716 - loss: 1.1948 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4745 - loss: 1.1739
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4761 - loss: 1.1647
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4793 - loss: 1.1582
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4817 - loss: 1.1544
[1m224/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4838 - loss: 1.1513
[1m262/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4854 - loss: 1.1489
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4868 - loss: 1.1470
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4876 - loss: 1.1458 - val_accuracy: 0.5400 - val_loss: 1.1126
Epoch 20/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0515
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4889 - loss: 1.1405 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4903 - loss: 1.1426
[1m109/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4905 - loss: 1.1407
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4903 - loss: 1.1393
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4907 - loss: 1.1375
[1m221/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4910 - loss: 1.1364
[1m262/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4913 - loss: 1.1353
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4918 - loss: 1.1344
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4919 - loss: 1.1340 - val_accuracy: 0.5404 - val_loss: 1.1194
Epoch 21/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 1.1264
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5180 - loss: 1.0902 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5092 - loss: 1.1074
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5049 - loss: 1.1150
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5023 - loss: 1.1181
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5014 - loss: 1.1192
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5013 - loss: 1.1199
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5014 - loss: 1.1204
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5014 - loss: 1.1208
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5014 - loss: 1.1210 - val_accuracy: 0.5446 - val_loss: 1.1046
Epoch 22/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4062 - loss: 1.1412
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4768 - loss: 1.1563 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4815 - loss: 1.1556
[1m106/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4860 - loss: 1.1509
[1m144/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4904 - loss: 1.1466
[1m179/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4935 - loss: 1.1432
[1m218/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4954 - loss: 1.1412
[1m257/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4966 - loss: 1.1392
[1m296/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4980 - loss: 1.1371
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4988 - loss: 1.1354 - val_accuracy: 0.5653 - val_loss: 1.0988
Epoch 23/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.4375 - loss: 1.0243
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4842 - loss: 1.0885 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4896 - loss: 1.0980
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4915 - loss: 1.1007
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4913 - loss: 1.1036
[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4910 - loss: 1.1062
[1m220/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4911 - loss: 1.1081
[1m260/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4915 - loss: 1.1100
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4928 - loss: 1.1114 - val_accuracy: 0.5604 - val_loss: 1.1018
Epoch 24/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.3438 - loss: 1.1642
[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4858 - loss: 1.1299 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4985 - loss: 1.1142
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5018 - loss: 1.1095
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5039 - loss: 1.1075
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5051 - loss: 1.1066
[1m222/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5054 - loss: 1.1070
[1m260/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5055 - loss: 1.1075
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5055 - loss: 1.1079
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5056 - loss: 1.1080 - val_accuracy: 0.5702 - val_loss: 1.1074
Epoch 25/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.2265
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5205 - loss: 1.1023 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5165 - loss: 1.1049
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5143 - loss: 1.1070
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5123 - loss: 1.1071
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5107 - loss: 1.1071
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5098 - loss: 1.1067
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5089 - loss: 1.1062
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5080 - loss: 1.1062
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5078 - loss: 1.1061 - val_accuracy: 0.5555 - val_loss: 1.0914
Epoch 26/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.5625 - loss: 1.0063
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5261 - loss: 1.0657 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5208 - loss: 1.0779
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5192 - loss: 1.0852
[1m161/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5180 - loss: 1.0883
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5162 - loss: 1.0909
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5145 - loss: 1.0937
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5132 - loss: 1.0956
[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5122 - loss: 1.0968
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5120 - loss: 1.0971 - val_accuracy: 0.5527 - val_loss: 1.1001
Epoch 27/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1336
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4851 - loss: 1.1334 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4942 - loss: 1.1220
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4953 - loss: 1.1204
[1m162/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4974 - loss: 1.1168
[1m205/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4996 - loss: 1.1128
[1m247/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5013 - loss: 1.1103
[1m285/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5025 - loss: 1.1087
[1m326/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5035 - loss: 1.1077
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5036 - loss: 1.1076 - val_accuracy: 0.5537 - val_loss: 1.0967
Epoch 28/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5433 - loss: 1.0817 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5365 - loss: 1.0861
[1m108/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5299 - loss: 1.0902
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5263 - loss: 1.0919
[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5244 - loss: 1.0916
[1m221/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5234 - loss: 1.0911
[1m258/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5222 - loss: 1.0912
[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5209 - loss: 1.0913
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5202 - loss: 1.0915 - val_accuracy: 0.5583 - val_loss: 1.1021
Epoch 29/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 26ms/step - accuracy: 0.3438 - loss: 1.2679
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4982 - loss: 1.0995 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5113 - loss: 1.0876
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5155 - loss: 1.0860
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5178 - loss: 1.0854
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5182 - loss: 1.0864
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5185 - loss: 1.0870
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5186 - loss: 1.0874
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5189 - loss: 1.0873
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Epoch 30/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 28ms/step - accuracy: 0.4375 - loss: 1.2374
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 401ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 16ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 16ms/step
Saved model to disk.

Accuracy capturado en la ejecución 12: 51.46 [%]
F1-score capturado en la ejecución 12: 50.41 [%]

=== EJECUCIÓN 13 ===

--- TRAIN (ejecución 13) ---

--- TEST (ejecución 13) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 15ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m70/89[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 730us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 862us/step
Global accuracy score (validation) = 57.23 [%]
Global F1 score (validation) = 56.27 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.220323   0.24941446 0.50431186 0.02595063]
 [0.18236081 0.13745622 0.6222004  0.05798263]
 [0.26390606 0.17450476 0.49109548 0.07049371]
 ...
 [0.20635465 0.16090925 0.56325436 0.06948179]
 [0.26092696 0.1435406  0.4724102  0.12312218]
 [0.15912172 0.10966983 0.70091546 0.03029301]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 55.12 [%]
Global accuracy score (test) = 50.55 [%]
Global F1 score (train) = 55.28 [%]
Global F1 score (test) = 50.32 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.38      0.29      0.33       400
MODERATE-INTENSITY       0.46      0.58      0.51       400
         SEDENTARY       0.52      0.68      0.59       400
VIGOROUS-INTENSITY       0.77      0.47      0.58       345

          accuracy                           0.51      1545
         macro avg       0.53      0.50      0.50      1545
      weighted avg       0.52      0.51      0.50      1545

2025-11-05 10:46:48.671072: 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`.
2025-11-05 10:46:48.682327: 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 00:00:1762336008.695463 3034744 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762336008.699471 3034744 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762336008.709405 3034744 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336008.709422 3034744 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336008.709431 3034744 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336008.709432 3034744 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:46:48.712594: 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 00:00:1762336010.932520 3034744 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 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..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/53
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762336012.550076 3034857 service.cc:152] XLA service 0x7fb85c00ccc0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762336012.550133 3034857 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:46:52.584346: 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 00:00:1762336012.751415 3034857 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762336014.910370 3034857 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18:15[0m 3s/step - accuracy: 0.2188 - loss: 3.2710
[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2670 - loss: 2.8301  
[1m 70/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2733 - loss: 2.6299
[1m107/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2766 - loss: 2.4895
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2802 - loss: 2.3667
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2838 - loss: 2.2769
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2864 - loss: 2.2036
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2883 - loss: 2.1413
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2897 - loss: 2.0862
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step - accuracy: 0.2903 - loss: 2.0641
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 10ms/step - accuracy: 0.2904 - loss: 2.0629 - val_accuracy: 0.4589 - val_loss: 1.2733
Epoch 2/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.3750 - loss: 1.3665
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3201 - loss: 1.3978 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3139 - loss: 1.4008
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3174 - loss: 1.3963
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3204 - loss: 1.3928
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3226 - loss: 1.3896
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3243 - loss: 1.3866
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3256 - loss: 1.3838
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3273 - loss: 1.3810
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3282 - loss: 1.3798 - val_accuracy: 0.4684 - val_loss: 1.2479
Epoch 3/53

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[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3535 - loss: 1.3413
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[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3523 - loss: 1.3410
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Epoch 4/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3552 - loss: 1.3271 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3682 - loss: 1.3123
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[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3786 - loss: 1.3029
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[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3841 - loss: 1.2977
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3855 - loss: 1.2960
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3866 - loss: 1.2947
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3870 - loss: 1.2942 - val_accuracy: 0.4821 - val_loss: 1.2024
Epoch 5/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3799 - loss: 1.2834 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3883 - loss: 1.2754
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3927 - loss: 1.2714
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3960 - loss: 1.2688
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3977 - loss: 1.2675
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3988 - loss: 1.2665
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3997 - loss: 1.2657
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4006 - loss: 1.2649
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4009 - loss: 1.2647 - val_accuracy: 0.4838 - val_loss: 1.1899
Epoch 6/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4436 - loss: 1.2173 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4411 - loss: 1.2245
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4381 - loss: 1.2284
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4362 - loss: 1.2297
[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4347 - loss: 1.2304
[1m224/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4331 - loss: 1.2316
[1m260/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4320 - loss: 1.2327
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4308 - loss: 1.2338
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4302 - loss: 1.2345 - val_accuracy: 0.5137 - val_loss: 1.1724
Epoch 7/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4291 - loss: 1.2430 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4284 - loss: 1.2440
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[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4284 - loss: 1.2388
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4290 - loss: 1.2364
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4296 - loss: 1.2348
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4304 - loss: 1.2333
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4310 - loss: 1.2320
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4311 - loss: 1.2316 - val_accuracy: 0.5049 - val_loss: 1.1512
Epoch 8/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4474 - loss: 1.1912 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4445 - loss: 1.1986
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4404 - loss: 1.2042
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4388 - loss: 1.2058
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4378 - loss: 1.2063
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4375 - loss: 1.2069
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4375 - loss: 1.2068
[1m317/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4373 - loss: 1.2073
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4372 - loss: 1.2074 - val_accuracy: 0.5049 - val_loss: 1.1664
Epoch 9/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1713
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4476 - loss: 1.2130 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4456 - loss: 1.2147
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4437 - loss: 1.2145
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4424 - loss: 1.2139
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4420 - loss: 1.2136
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4423 - loss: 1.2128
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4426 - loss: 1.2120
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4430 - loss: 1.2112
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4432 - loss: 1.2106 - val_accuracy: 0.5025 - val_loss: 1.1544
Epoch 10/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5938 - loss: 1.0153
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4612 - loss: 1.1846 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4497 - loss: 1.1974
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4452 - loss: 1.2011
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4423 - loss: 1.2034
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4401 - loss: 1.2044
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4390 - loss: 1.2047
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4388 - loss: 1.2045
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4387 - loss: 1.2039
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4387 - loss: 1.2037 - val_accuracy: 0.5070 - val_loss: 1.1600
Epoch 11/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3750 - loss: 1.2347
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4697 - loss: 1.2038 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4607 - loss: 1.1998
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4586 - loss: 1.1966
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4566 - loss: 1.1951
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4552 - loss: 1.1954
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4545 - loss: 1.1951
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4543 - loss: 1.1949
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4543 - loss: 1.1946
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4544 - loss: 1.1943 - val_accuracy: 0.5179 - val_loss: 1.1505
Epoch 12/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.2028
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4396 - loss: 1.2157 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4405 - loss: 1.2077
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4432 - loss: 1.2021
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4466 - loss: 1.1985
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4481 - loss: 1.1963
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4496 - loss: 1.1937
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4505 - loss: 1.1918
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4512 - loss: 1.1901
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4515 - loss: 1.1893 - val_accuracy: 0.5200 - val_loss: 1.1340
Epoch 13/53

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[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4556 - loss: 1.1830 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4538 - loss: 1.1860
[1m109/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4566 - loss: 1.1836
[1m142/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4583 - loss: 1.1810
[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4588 - loss: 1.1799
[1m217/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4591 - loss: 1.1790
[1m258/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4597 - loss: 1.1783
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4602 - loss: 1.1778
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4603 - loss: 1.1778 - val_accuracy: 0.5218 - val_loss: 1.1404
Epoch 14/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1826
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4920 - loss: 1.1434 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4765 - loss: 1.1579
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4729 - loss: 1.1622
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4711 - loss: 1.1639
[1m201/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4694 - loss: 1.1650
[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4682 - loss: 1.1651
[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4675 - loss: 1.1650
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4673 - loss: 1.1647
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4672 - loss: 1.1647 - val_accuracy: 0.5239 - val_loss: 1.1469
Epoch 15/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3750 - loss: 1.2519
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4764 - loss: 1.1559 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4814 - loss: 1.1523
[1m123/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4793 - loss: 1.1545
[1m163/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4773 - loss: 1.1550
[1m202/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4764 - loss: 1.1550
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4756 - loss: 1.1553
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4750 - loss: 1.1556
[1m319/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4748 - loss: 1.1557
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4747 - loss: 1.1557 - val_accuracy: 0.5291 - val_loss: 1.1442
Epoch 16/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.0622
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4721 - loss: 1.1531 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4744 - loss: 1.1517
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4751 - loss: 1.1552
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4740 - loss: 1.1582
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4722 - loss: 1.1609
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4712 - loss: 1.1620
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4711 - loss: 1.1619
[1m317/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4714 - loss: 1.1617
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4715 - loss: 1.1616 - val_accuracy: 0.5242 - val_loss: 1.1414
Epoch 17/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.2055
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4814 - loss: 1.1261 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4831 - loss: 1.1241
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4846 - loss: 1.1264
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4841 - loss: 1.1304
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4832 - loss: 1.1335
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4827 - loss: 1.1356
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4825 - loss: 1.1374
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4820 - loss: 1.1388
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4818 - loss: 1.1393 - val_accuracy: 0.5499 - val_loss: 1.1255
Epoch 18/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5075 - loss: 1.1005 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4995 - loss: 1.1135
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4943 - loss: 1.1182
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4920 - loss: 1.1208
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4900 - loss: 1.1238
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4881 - loss: 1.1273
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4869 - loss: 1.1294
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4858 - loss: 1.1311
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4854 - loss: 1.1318 - val_accuracy: 0.5432 - val_loss: 1.1349
Epoch 19/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.0279
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4801 - loss: 1.1480 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4775 - loss: 1.1498
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4779 - loss: 1.1484
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4789 - loss: 1.1471
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4801 - loss: 1.1456
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4804 - loss: 1.1451
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4799 - loss: 1.1450
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4797 - loss: 1.1449
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4797 - loss: 1.1447 - val_accuracy: 0.5562 - val_loss: 1.1281
Epoch 20/53

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5293 - loss: 1.0593 
[1m 71/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5151 - loss: 1.0914
[1m107/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5093 - loss: 1.1055
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5048 - loss: 1.1138
[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5020 - loss: 1.1189
[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5002 - loss: 1.1216
[1m259/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4994 - loss: 1.1229
[1m296/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4987 - loss: 1.1236
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4982 - loss: 1.1242 - val_accuracy: 0.5611 - val_loss: 1.1310
Epoch 21/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3750 - loss: 1.2265
[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4669 - loss: 1.1485 
[1m 71/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4769 - loss: 1.1430
[1m109/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4790 - loss: 1.1433
[1m146/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4804 - loss: 1.1419
[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4804 - loss: 1.1413
[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4804 - loss: 1.1414
[1m262/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4809 - loss: 1.1413
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4814 - loss: 1.1411
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4816 - loss: 1.1408 - val_accuracy: 0.5400 - val_loss: 1.1128
Epoch 22/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4062 - loss: 1.1916
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5006 - loss: 1.1124 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5049 - loss: 1.1069
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5007 - loss: 1.1122
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4968 - loss: 1.1172
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4950 - loss: 1.1197
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4937 - loss: 1.1211
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4926 - loss: 1.1223
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4918 - loss: 1.1232
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4916 - loss: 1.1234 - val_accuracy: 0.5211 - val_loss: 1.1261
Epoch 23/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4829 - loss: 1.1299 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4892 - loss: 1.1227
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4913 - loss: 1.1203
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4921 - loss: 1.1186
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4930 - loss: 1.1175
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4934 - loss: 1.1168
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4934 - loss: 1.1169
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4931 - loss: 1.1175
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4930 - loss: 1.1179 - val_accuracy: 0.5376 - val_loss: 1.1171
Epoch 24/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1604
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4980 - loss: 1.1262 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4942 - loss: 1.1258
[1m123/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4927 - loss: 1.1247
[1m165/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4907 - loss: 1.1251
[1m205/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4904 - loss: 1.1251
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4907 - loss: 1.1247
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4912 - loss: 1.1243
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4916 - loss: 1.1237
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4917 - loss: 1.1235 - val_accuracy: 0.5643 - val_loss: 1.1171
Epoch 25/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.0450
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5068 - loss: 1.0867 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5090 - loss: 1.0950
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5103 - loss: 1.0989
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5103 - loss: 1.1003
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5100 - loss: 1.1020
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5099 - loss: 1.1030
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5097 - loss: 1.1043
[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5092 - loss: 1.1056
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5090 - loss: 1.1059 - val_accuracy: 0.5492 - val_loss: 1.1100
Epoch 26/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.2981
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5136 - loss: 1.0911 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5119 - loss: 1.0891
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5118 - loss: 1.0909
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5116 - loss: 1.0923
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5107 - loss: 1.0938
[1m226/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5101 - loss: 1.0949
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5095 - loss: 1.0955
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5093 - loss: 1.0960
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5091 - loss: 1.0964 - val_accuracy: 0.5544 - val_loss: 1.1057
Epoch 27/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 1.0891
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5298 - loss: 1.0961 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5216 - loss: 1.1024
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5190 - loss: 1.1017
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5162 - loss: 1.1006
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5145 - loss: 1.1004
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5143 - loss: 1.0992
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5138 - loss: 1.0990
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5136 - loss: 1.0991
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5135 - loss: 1.0991 - val_accuracy: 0.5499 - val_loss: 1.1104
Epoch 28/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4602 - loss: 1.1309 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4746 - loss: 1.1247
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4839 - loss: 1.1175
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4874 - loss: 1.1134
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4911 - loss: 1.1093
[1m224/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4938 - loss: 1.1074
[1m262/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4961 - loss: 1.1058
[1m296/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4981 - loss: 1.1042
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4994 - loss: 1.1032 - val_accuracy: 0.5674 - val_loss: 1.0994
Epoch 29/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 25ms/step - accuracy: 0.2500 - loss: 1.4389
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4472 - loss: 1.1518 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4714 - loss: 1.1310
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4801 - loss: 1.1240
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4852 - loss: 1.1201
[1m199/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4893 - loss: 1.1160
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4919 - loss: 1.1128
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4938 - loss: 1.1105
[1m317/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4958 - loss: 1.1084
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4963 - loss: 1.1078 - val_accuracy: 0.5400 - val_loss: 1.1042
Epoch 30/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5436 - loss: 1.0334 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5291 - loss: 1.0598
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5256 - loss: 1.0688
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5226 - loss: 1.0753
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5205 - loss: 1.0792
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5195 - loss: 1.0816
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5185 - loss: 1.0840
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5180 - loss: 1.0855
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5177 - loss: 1.0861 - val_accuracy: 0.5558 - val_loss: 1.0974
Epoch 31/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5625 - loss: 1.1344
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5331 - loss: 1.0890 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5308 - loss: 1.0798
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5293 - loss: 1.0776
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5268 - loss: 1.0786
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5247 - loss: 1.0802
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5232 - loss: 1.0808
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5223 - loss: 1.0810
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5215 - loss: 1.0812
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5210 - loss: 1.0814 - val_accuracy: 0.5744 - val_loss: 1.1029
Epoch 32/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1478
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5328 - loss: 1.0680 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5278 - loss: 1.0723
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5236 - loss: 1.0753
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5210 - loss: 1.0769
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5202 - loss: 1.0778
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5193 - loss: 1.0784
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5184 - loss: 1.0787
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5175 - loss: 1.0794
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5173 - loss: 1.0795 - val_accuracy: 0.5748 - val_loss: 1.1114
Epoch 33/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5400 - loss: 1.0823 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5320 - loss: 1.0801
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5286 - loss: 1.0799
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5275 - loss: 1.0789
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5271 - loss: 1.0784
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5265 - loss: 1.0787
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5258 - loss: 1.0795
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5252 - loss: 1.0798
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5249 - loss: 1.0799 - val_accuracy: 0.5562 - val_loss: 1.0969
Epoch 34/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.5625 - loss: 1.1169
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5301 - loss: 1.0672 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5223 - loss: 1.0787
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5226 - loss: 1.0786
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5218 - loss: 1.0779
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5217 - loss: 1.0771
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5219 - loss: 1.0762
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5220 - loss: 1.0756
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5222 - loss: 1.0749
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5221 - loss: 1.0747 - val_accuracy: 0.5562 - val_loss: 1.1034
Epoch 35/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4684 - loss: 1.1163 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4789 - loss: 1.1027
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4844 - loss: 1.0958
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4872 - loss: 1.0929
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4892 - loss: 1.0916
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4920 - loss: 1.0901
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4951 - loss: 1.0881
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4979 - loss: 1.0861
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4988 - loss: 1.0855 - val_accuracy: 0.5544 - val_loss: 1.0999
Epoch 36/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1169
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5302 - loss: 1.0649 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5284 - loss: 1.0725
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5293 - loss: 1.0736
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5287 - loss: 1.0738
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5284 - loss: 1.0736
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5279 - loss: 1.0734
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5278 - loss: 1.0729
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5280 - loss: 1.0723
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5280 - loss: 1.0722 - val_accuracy: 0.5657 - val_loss: 1.1020
Epoch 37/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1538
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5458 - loss: 1.0529 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5408 - loss: 1.0537
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5354 - loss: 1.0591
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5327 - loss: 1.0631
[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5314 - loss: 1.0651
[1m220/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5302 - loss: 1.0667
[1m254/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5296 - loss: 1.0672
[1m295/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5291 - loss: 1.0673
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5289 - loss: 1.0671 - val_accuracy: 0.5593 - val_loss: 1.0966
Epoch 38/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5065 - loss: 1.0567 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5106 - loss: 1.0652
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5136 - loss: 1.0639
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5160 - loss: 1.0628
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[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5184 - loss: 1.0647
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5193 - loss: 1.0650
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5198 - loss: 1.0650 - val_accuracy: 0.5758 - val_loss: 1.0808
Epoch 39/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6250 - loss: 0.9456
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5295 - loss: 1.0697 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5311 - loss: 1.0716
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[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5337 - loss: 1.0712
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5334 - loss: 1.0704
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5335 - loss: 1.0692
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5339 - loss: 1.0677
[1m317/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5345 - loss: 1.0664
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5347 - loss: 1.0660 - val_accuracy: 0.5713 - val_loss: 1.1009
Epoch 40/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5560 - loss: 1.0333 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5400 - loss: 1.0475
[1m110/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5331 - loss: 1.0549
[1m145/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5296 - loss: 1.0585
[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5279 - loss: 1.0594
[1m218/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5271 - loss: 1.0593
[1m256/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5271 - loss: 1.0591
[1m296/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5275 - loss: 1.0584
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5278 - loss: 1.0580 - val_accuracy: 0.5657 - val_loss: 1.0877
Epoch 41/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.5312 - loss: 0.9946
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5095 - loss: 1.0683 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5156 - loss: 1.0623
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5207 - loss: 1.0574
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5238 - loss: 1.0551
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5266 - loss: 1.0534
[1m226/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5289 - loss: 1.0517
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5304 - loss: 1.0509
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5316 - loss: 1.0505
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5320 - loss: 1.0504 - val_accuracy: 0.5762 - val_loss: 1.0948
Epoch 42/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 0.9069
[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5250 - loss: 1.0259 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5293 - loss: 1.0367
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[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5329 - loss: 1.0436
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[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5342 - loss: 1.0457
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5348 - loss: 1.0456
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5356 - loss: 1.0451
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5360 - loss: 1.0449 - val_accuracy: 0.5485 - val_loss: 1.1039
Epoch 43/53

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Saved model to disk.

Accuracy capturado en la ejecución 13: 50.55 [%]
F1-score capturado en la ejecución 13: 50.32 [%]

=== EJECUCIÓN 14 ===

--- TRAIN (ejecución 14) ---

--- TEST (ejecución 14) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m59/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 876us/step
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Global accuracy score (validation) = 56.0 [%]
Global F1 score (validation) = 54.98 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.15392117 0.14182332 0.65492344 0.04933206]
 [0.36835733 0.4104116  0.20669821 0.01453277]
 [0.16741146 0.1259238  0.6335877  0.07307696]
 ...
 [0.20157136 0.15531306 0.5497772  0.0933384 ]
 [0.22823477 0.12902209 0.47753322 0.1652099 ]
 [0.1817616  0.14463963 0.6042558  0.06934294]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 57.79 [%]
Global accuracy score (test) = 51.59 [%]
Global F1 score (train) = 57.8 [%]
Global F1 score (test) = 51.08 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.36      0.28      0.31       400
MODERATE-INTENSITY       0.50      0.56      0.53       400
         SEDENTARY       0.51      0.75      0.60       400
VIGOROUS-INTENSITY       0.79      0.48      0.60       345

          accuracy                           0.52      1545
         macro avg       0.54      0.51      0.51      1545
      weighted avg       0.53      0.52      0.51      1545

2025-11-05 10:47:34.044634: 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`.
2025-11-05 10:47:34.056036: 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 00:00:1762336054.069235 3039738 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762336054.073500 3039738 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762336054.083442 3039738 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336054.083465 3039738 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336054.083468 3039738 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336054.083469 3039738 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:47:34.086664: 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 00:00:1762336056.296280 3039738 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 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..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/53
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762336057.948937 3039847 service.cc:152] XLA service 0x728bb800d590 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762336057.948967 3039847 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:47:37.982147: 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 00:00:1762336058.156837 3039847 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762336060.364706 3039847 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 2/53

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[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3323 - loss: 1.3728
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[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3396 - loss: 1.3618
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3414 - loss: 1.3600
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Epoch 3/53

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[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3740 - loss: 1.3212
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3745 - loss: 1.3174
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3746 - loss: 1.3158
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[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3754 - loss: 1.3131
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3761 - loss: 1.3121
[1m321/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3767 - loss: 1.3112
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Epoch 4/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3923 - loss: 1.3314 
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[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3927 - loss: 1.3063
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3934 - loss: 1.3017
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3945 - loss: 1.2983
[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3957 - loss: 1.2960
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3965 - loss: 1.2941
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3975 - loss: 1.2922
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Epoch 5/53

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[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4139 - loss: 1.2640
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4145 - loss: 1.2609
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4159 - loss: 1.2595
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[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4183 - loss: 1.2579
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Epoch 6/53

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4041 - loss: 1.2291 
[1m 65/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4020 - loss: 1.2397
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[1m141/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4082 - loss: 1.2456
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[1m260/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4135 - loss: 1.2454
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4153 - loss: 1.2446
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Epoch 7/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4469 - loss: 1.1864 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4454 - loss: 1.1908
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[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4467 - loss: 1.1963
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4475 - loss: 1.1983
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4473 - loss: 1.1999
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4471 - loss: 1.2017
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4468 - loss: 1.2035
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4464 - loss: 1.2044 - val_accuracy: 0.4846 - val_loss: 1.1634
Epoch 8/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4406 - loss: 1.2320 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4409 - loss: 1.2219
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4436 - loss: 1.2160
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4446 - loss: 1.2133
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4454 - loss: 1.2112
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4456 - loss: 1.2105
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4458 - loss: 1.2094
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4459 - loss: 1.2090
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4458 - loss: 1.2089 - val_accuracy: 0.5067 - val_loss: 1.1524
Epoch 9/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4654 - loss: 1.1612 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4592 - loss: 1.1716
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4586 - loss: 1.1720
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4581 - loss: 1.1734
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4573 - loss: 1.1758
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4569 - loss: 1.1778
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4568 - loss: 1.1793
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4569 - loss: 1.1806
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4569 - loss: 1.1813 - val_accuracy: 0.5095 - val_loss: 1.1468
Epoch 10/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4567 - loss: 1.1710 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4565 - loss: 1.1715
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4577 - loss: 1.1716
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4607 - loss: 1.1714
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4618 - loss: 1.1719
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[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4619 - loss: 1.1738
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4618 - loss: 1.1743
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4617 - loss: 1.1749 - val_accuracy: 0.5130 - val_loss: 1.1578
Epoch 11/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4439 - loss: 1.1911 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4476 - loss: 1.1867
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4514 - loss: 1.1834
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4535 - loss: 1.1821
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4551 - loss: 1.1816
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4561 - loss: 1.1815
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4571 - loss: 1.1815
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4576 - loss: 1.1817
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4579 - loss: 1.1818 - val_accuracy: 0.5116 - val_loss: 1.1346
Epoch 12/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4705 - loss: 1.1927 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4680 - loss: 1.1893
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4704 - loss: 1.1843
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4724 - loss: 1.1804
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4727 - loss: 1.1789
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4729 - loss: 1.1782
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4727 - loss: 1.1773
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4727 - loss: 1.1764
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4726 - loss: 1.1759 - val_accuracy: 0.5119 - val_loss: 1.1329
Epoch 13/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.6875 - loss: 0.8478
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5275 - loss: 1.0963 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5041 - loss: 1.1246
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4934 - loss: 1.1364
[1m161/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4874 - loss: 1.1441
[1m201/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4833 - loss: 1.1499
[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4809 - loss: 1.1532
[1m282/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4790 - loss: 1.1554
[1m324/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4773 - loss: 1.1573
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4771 - loss: 1.1575 - val_accuracy: 0.5197 - val_loss: 1.1320
Epoch 14/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5625 - loss: 1.0746
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5126 - loss: 1.1324 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4954 - loss: 1.1494
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4906 - loss: 1.1524
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4884 - loss: 1.1532
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4870 - loss: 1.1540
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4859 - loss: 1.1548
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4851 - loss: 1.1555
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4845 - loss: 1.1555
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4842 - loss: 1.1556 - val_accuracy: 0.5348 - val_loss: 1.1485
Epoch 15/53

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[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4663 - loss: 1.1685 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4715 - loss: 1.1624
[1m109/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4727 - loss: 1.1599
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4724 - loss: 1.1581
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4731 - loss: 1.1566
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[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4744 - loss: 1.1556
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4749 - loss: 1.1556
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4751 - loss: 1.1556 - val_accuracy: 0.5176 - val_loss: 1.1342
Epoch 16/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4875 - loss: 1.1559 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4838 - loss: 1.1581
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4826 - loss: 1.1566
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4821 - loss: 1.1552
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4819 - loss: 1.1536
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4818 - loss: 1.1528
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4816 - loss: 1.1525
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4813 - loss: 1.1522
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4813 - loss: 1.1519 - val_accuracy: 0.5400 - val_loss: 1.1326
Epoch 17/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5028 - loss: 1.1279 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5001 - loss: 1.1308
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4963 - loss: 1.1351
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4942 - loss: 1.1375
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[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4918 - loss: 1.1387
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4910 - loss: 1.1390
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4905 - loss: 1.1390
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4904 - loss: 1.1390 - val_accuracy: 0.5369 - val_loss: 1.1245
Epoch 18/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.0280
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4706 - loss: 1.1502 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4830 - loss: 1.1361
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4878 - loss: 1.1312
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4895 - loss: 1.1295
[1m199/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4897 - loss: 1.1293
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4893 - loss: 1.1300
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4888 - loss: 1.1312
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4885 - loss: 1.1320
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4883 - loss: 1.1324 - val_accuracy: 0.5309 - val_loss: 1.1317
Epoch 19/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4772 - loss: 1.1285 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4783 - loss: 1.1351
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4805 - loss: 1.1365
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4824 - loss: 1.1372
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4836 - loss: 1.1377
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4844 - loss: 1.1370
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4850 - loss: 1.1359
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4853 - loss: 1.1350
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4854 - loss: 1.1347 - val_accuracy: 0.5558 - val_loss: 1.1186
Epoch 20/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4782 - loss: 1.1526 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4833 - loss: 1.1429
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4828 - loss: 1.1437
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4840 - loss: 1.1426
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4851 - loss: 1.1415
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4866 - loss: 1.1407
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4878 - loss: 1.1400
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4887 - loss: 1.1388
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4890 - loss: 1.1384 - val_accuracy: 0.5418 - val_loss: 1.1216
Epoch 21/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1002
[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4945 - loss: 1.1194 
[1m 69/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4861 - loss: 1.1254
[1m109/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4856 - loss: 1.1252
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4859 - loss: 1.1232
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4858 - loss: 1.1225
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4860 - loss: 1.1226
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4862 - loss: 1.1229
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4867 - loss: 1.1233
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4869 - loss: 1.1236 - val_accuracy: 0.5242 - val_loss: 1.1200
Epoch 22/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6250 - loss: 0.9507
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5411 - loss: 1.0643 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5280 - loss: 1.0793
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5181 - loss: 1.0900
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5123 - loss: 1.0968
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5083 - loss: 1.1014
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5050 - loss: 1.1050
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5028 - loss: 1.1075
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5015 - loss: 1.1090
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5008 - loss: 1.1099 - val_accuracy: 0.5523 - val_loss: 1.1154
Epoch 23/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 0.9894
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5293 - loss: 1.0595 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5079 - loss: 1.0975
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5008 - loss: 1.1093
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4991 - loss: 1.1131
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4990 - loss: 1.1138
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4994 - loss: 1.1139
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5000 - loss: 1.1139
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5004 - loss: 1.1142
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5005 - loss: 1.1144 - val_accuracy: 0.5453 - val_loss: 1.1211
Epoch 24/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3750 - loss: 1.2145
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4744 - loss: 1.1273 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4819 - loss: 1.1147
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4851 - loss: 1.1110
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4872 - loss: 1.1092
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4887 - loss: 1.1088
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4896 - loss: 1.1087
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4908 - loss: 1.1082
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4920 - loss: 1.1080
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4923 - loss: 1.1080 - val_accuracy: 0.5593 - val_loss: 1.1184
Epoch 25/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4843 - loss: 1.0867 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4919 - loss: 1.0958
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4958 - loss: 1.0989
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4979 - loss: 1.1000
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4993 - loss: 1.1009
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5004 - loss: 1.1009
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5012 - loss: 1.1008
[1m317/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5016 - loss: 1.1007
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5018 - loss: 1.1007 - val_accuracy: 0.5446 - val_loss: 1.0921
Epoch 26/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1862
[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5107 - loss: 1.1067 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5039 - loss: 1.1129
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5041 - loss: 1.1108
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5045 - loss: 1.1104
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5055 - loss: 1.1095
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5063 - loss: 1.1092
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5065 - loss: 1.1088
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5070 - loss: 1.1081
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5072 - loss: 1.1077 - val_accuracy: 0.5386 - val_loss: 1.1090
Epoch 27/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4979 - loss: 1.0973 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5095 - loss: 1.0906
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5139 - loss: 1.0920
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5148 - loss: 1.0936
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[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5142 - loss: 1.0956
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5142 - loss: 1.0960
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5144 - loss: 1.0962
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5145 - loss: 1.0963 - val_accuracy: 0.5629 - val_loss: 1.1219
Epoch 28/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5938 - loss: 1.0512
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5417 - loss: 1.0731 
[1m 69/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5314 - loss: 1.0802
[1m105/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5259 - loss: 1.0834
[1m141/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5228 - loss: 1.0852
[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5205 - loss: 1.0870
[1m220/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5185 - loss: 1.0882
[1m259/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5172 - loss: 1.0886
[1m296/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5163 - loss: 1.0891
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5158 - loss: 1.0892 - val_accuracy: 0.5341 - val_loss: 1.1063
Epoch 29/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5000 - loss: 1.1317
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4970 - loss: 1.1090 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5034 - loss: 1.1032
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5050 - loss: 1.1015
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5069 - loss: 1.0986
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5077 - loss: 1.0974
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5087 - loss: 1.0965
[1m261/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5097 - loss: 1.0957
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5107 - loss: 1.0948
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5113 - loss: 1.0944 - val_accuracy: 0.5460 - val_loss: 1.0913
Epoch 30/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5249 - loss: 1.0575 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5237 - loss: 1.0620
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5221 - loss: 1.0705
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5213 - loss: 1.0753
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[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5217 - loss: 1.0808
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5219 - loss: 1.0824
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5218 - loss: 1.0838
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5217 - loss: 1.0846 - val_accuracy: 0.5446 - val_loss: 1.1022
Epoch 31/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5054 - loss: 1.0727 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5153 - loss: 1.0697
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[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5213 - loss: 1.0695
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[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5205 - loss: 1.0739
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5202 - loss: 1.0748
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Epoch 32/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4986 - loss: 1.0547 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5087 - loss: 1.0596
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[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5108 - loss: 1.0658
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[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5116 - loss: 1.0681
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5118 - loss: 1.0693
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5120 - loss: 1.0707
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5121 - loss: 1.0712 - val_accuracy: 0.5713 - val_loss: 1.0972
Epoch 33/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 1.0010
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5383 - loss: 1.0308 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5291 - loss: 1.0562
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5267 - loss: 1.0655
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5258 - loss: 1.0679
[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5262 - loss: 1.0681
[1m224/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5262 - loss: 1.0684
[1m263/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5264 - loss: 1.0686
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5265 - loss: 1.0689
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5264 - loss: 1.0691 - val_accuracy: 0.5706 - val_loss: 1.0891
Epoch 34/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5625 - loss: 1.0526
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5466 - loss: 1.0637 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5404 - loss: 1.0724
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[1m161/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5353 - loss: 1.0761
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[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5311 - loss: 1.0774
[1m319/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5308 - loss: 1.0773
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5307 - loss: 1.0773 - val_accuracy: 0.5744 - val_loss: 1.0894
Epoch 35/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5068 - loss: 1.0689 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5216 - loss: 1.0638
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5237 - loss: 1.0639
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5240 - loss: 1.0645
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[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5236 - loss: 1.0662
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5238 - loss: 1.0664
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5239 - loss: 1.0667
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5240 - loss: 1.0667 - val_accuracy: 0.5597 - val_loss: 1.0891
Epoch 36/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5513 - loss: 1.0267 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5442 - loss: 1.0351
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5426 - loss: 1.0358
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5398 - loss: 1.0382
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[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5353 - loss: 1.0432
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5342 - loss: 1.0456
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5335 - loss: 1.0475
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5332 - loss: 1.0485 - val_accuracy: 0.5688 - val_loss: 1.0792
Epoch 37/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5270 - loss: 1.0630 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5296 - loss: 1.0654
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5307 - loss: 1.0640
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5311 - loss: 1.0641
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[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5295 - loss: 1.0658
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5290 - loss: 1.0662
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5289 - loss: 1.0662
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5289 - loss: 1.0661 - val_accuracy: 0.5748 - val_loss: 1.1010
Epoch 38/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3125 - loss: 1.4339
[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5170 - loss: 1.0757 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5324 - loss: 1.0575
[1m110/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5389 - loss: 1.0516
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5422 - loss: 1.0498
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5420 - loss: 1.0499
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5409 - loss: 1.0512
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5398 - loss: 1.0524
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5388 - loss: 1.0534
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5383 - loss: 1.0539 - val_accuracy: 0.5748 - val_loss: 1.0911
Epoch 39/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.4375 - loss: 1.2789
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5254 - loss: 1.0686 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5278 - loss: 1.0651
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5302 - loss: 1.0615
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5327 - loss: 1.0585
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[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5354 - loss: 1.0553
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5356 - loss: 1.0555
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5355 - loss: 1.0555
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5355 - loss: 1.0555 - val_accuracy: 0.5741 - val_loss: 1.0941
Epoch 40/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5315 - loss: 1.0599 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5345 - loss: 1.0618
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5354 - loss: 1.0615
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5366 - loss: 1.0605
[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5376 - loss: 1.0590
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[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5391 - loss: 1.0569
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Epoch 41/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 1.0032
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[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5661 - loss: 1.0179
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 395ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 16ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 16ms/step
Saved model to disk.

Accuracy capturado en la ejecución 14: 51.59 [%]
F1-score capturado en la ejecución 14: 51.08 [%]

=== EJECUCIÓN 15 ===

--- TRAIN (ejecución 15) ---

--- TEST (ejecución 15) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:28[0m 1s/step
[1m 71/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 718us/step
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[1m258/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 785us/step
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 15ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m63/89[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 809us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 908us/step
Global accuracy score (validation) = 56.57 [%]
Global F1 score (validation) = 55.33 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.16773288 0.09106959 0.6753902  0.06580739]
 [0.3108148  0.22559083 0.31968623 0.1439082 ]
 [0.22007911 0.21029085 0.54472876 0.02490127]
 ...
 [0.21274884 0.20499647 0.5150659  0.06718874]
 [0.22510874 0.11574264 0.5494292  0.10971936]
 [0.16573906 0.10295801 0.674143   0.05715997]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 56.64 [%]
Global accuracy score (test) = 51.26 [%]
Global F1 score (train) = 56.47 [%]
Global F1 score (test) = 50.17 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.39      0.26      0.31       400
MODERATE-INTENSITY       0.48      0.56      0.51       400
         SEDENTARY       0.51      0.77      0.61       400
VIGOROUS-INTENSITY       0.76      0.46      0.57       345

          accuracy                           0.51      1545
         macro avg       0.53      0.51      0.50      1545
      weighted avg       0.53      0.51      0.50      1545

2025-11-05 10:48:18.239347: 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`.
2025-11-05 10:48:18.250694: 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 00:00:1762336098.264154 3044524 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762336098.268430 3044524 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762336098.278311 3044524 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336098.278330 3044524 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336098.278333 3044524 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336098.278335 3044524 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:48:18.281610: 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 00:00:1762336100.527538 3044524 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 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..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/53
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762336102.175640 3044654 service.cc:152] XLA service 0x75af8400c580 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762336102.175710 3044654 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:48:22.213842: 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 00:00:1762336102.394621 3044654 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762336104.566261 3044654 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 2/53

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Epoch 3/53

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Epoch 4/53

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[1m110/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4100 - loss: 1.2793
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4086 - loss: 1.2785
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4077 - loss: 1.2786
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[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4061 - loss: 1.2797
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Epoch 5/53

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[1m 42/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4276 - loss: 1.2792 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4175 - loss: 1.2781
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[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4124 - loss: 1.2742
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[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4114 - loss: 1.2691
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4115 - loss: 1.2681
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Epoch 6/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3946 - loss: 1.2596 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4136 - loss: 1.2436
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4192 - loss: 1.2388
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4212 - loss: 1.2371
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[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4226 - loss: 1.2370
[1m263/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4227 - loss: 1.2371
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4228 - loss: 1.2370
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4228 - loss: 1.2370 - val_accuracy: 0.5323 - val_loss: 1.1714
Epoch 7/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4405 - loss: 1.2390 
[1m 84/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4379 - loss: 1.2391
[1m125/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4390 - loss: 1.2347
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4391 - loss: 1.2324
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4382 - loss: 1.2319
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4377 - loss: 1.2312
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4373 - loss: 1.2305
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4371 - loss: 1.2299
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4370 - loss: 1.2297 - val_accuracy: 0.5186 - val_loss: 1.1746
Epoch 8/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.2078
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4891 - loss: 1.1866 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4775 - loss: 1.1900
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4705 - loss: 1.1960
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4651 - loss: 1.2001
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4620 - loss: 1.2013
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4592 - loss: 1.2027
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4566 - loss: 1.2042
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4539 - loss: 1.2063
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4528 - loss: 1.2071 - val_accuracy: 0.5172 - val_loss: 1.1644
Epoch 9/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4634 - loss: 1.2071 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4564 - loss: 1.2121
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4519 - loss: 1.2113
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4493 - loss: 1.2109
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4482 - loss: 1.2097
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4471 - loss: 1.2085
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4467 - loss: 1.2071
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4469 - loss: 1.2056 - val_accuracy: 0.5144 - val_loss: 1.1618
Epoch 10/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4379 - loss: 1.2064 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4510 - loss: 1.1966
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4554 - loss: 1.1935
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4545 - loss: 1.1958
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4539 - loss: 1.1975
[1m226/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4531 - loss: 1.1986
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4519 - loss: 1.1994
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4511 - loss: 1.1998
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4508 - loss: 1.1998 - val_accuracy: 0.5207 - val_loss: 1.1475
Epoch 11/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4584 - loss: 1.1620 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4642 - loss: 1.1640
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4687 - loss: 1.1665
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4705 - loss: 1.1674
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4707 - loss: 1.1688
[1m226/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4706 - loss: 1.1701
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4706 - loss: 1.1713
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4704 - loss: 1.1724
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4703 - loss: 1.1731 - val_accuracy: 0.5039 - val_loss: 1.1536
Epoch 12/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4375 - loss: 1.1904
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4471 - loss: 1.1891 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4523 - loss: 1.1891
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4522 - loss: 1.1900
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4528 - loss: 1.1907
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4539 - loss: 1.1903
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4552 - loss: 1.1885
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4563 - loss: 1.1869
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4571 - loss: 1.1860
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4575 - loss: 1.1857 - val_accuracy: 0.5260 - val_loss: 1.1322
Epoch 13/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3438 - loss: 1.2952
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4410 - loss: 1.1832 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4496 - loss: 1.1828
[1m107/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4515 - loss: 1.1850
[1m145/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4539 - loss: 1.1840
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4562 - loss: 1.1841
[1m224/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4574 - loss: 1.1840
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4577 - loss: 1.1840
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4576 - loss: 1.1841
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4577 - loss: 1.1839 - val_accuracy: 0.5225 - val_loss: 1.1407
Epoch 14/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4675 - loss: 1.1601 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4702 - loss: 1.1617
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4697 - loss: 1.1646
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4685 - loss: 1.1668
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4681 - loss: 1.1679
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4682 - loss: 1.1683
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4684 - loss: 1.1682
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4684 - loss: 1.1685 - val_accuracy: 0.5393 - val_loss: 1.1278
Epoch 15/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5016 - loss: 1.1467 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4878 - loss: 1.1566
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4850 - loss: 1.1565
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4846 - loss: 1.1552
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[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4826 - loss: 1.1560
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4820 - loss: 1.1560
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4815 - loss: 1.1558
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4813 - loss: 1.1558 - val_accuracy: 0.5421 - val_loss: 1.1331
Epoch 16/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4629 - loss: 1.1261 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4702 - loss: 1.1321
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4712 - loss: 1.1357
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4721 - loss: 1.1374
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[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4743 - loss: 1.1403
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4748 - loss: 1.1422
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4751 - loss: 1.1438
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4750 - loss: 1.1451 - val_accuracy: 0.5534 - val_loss: 1.1184
Epoch 17/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4690 - loss: 1.1763 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4722 - loss: 1.1650
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4720 - loss: 1.1616
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4722 - loss: 1.1593
[1m200/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4734 - loss: 1.1579
[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4739 - loss: 1.1573
[1m282/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4746 - loss: 1.1563
[1m323/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4750 - loss: 1.1558
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4750 - loss: 1.1558 - val_accuracy: 0.5330 - val_loss: 1.1319
Epoch 18/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.5625 - loss: 1.0103
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5010 - loss: 1.1193 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4952 - loss: 1.1135
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4933 - loss: 1.1162
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4910 - loss: 1.1196
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4891 - loss: 1.1225
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[1m260/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4869 - loss: 1.1270
[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4862 - loss: 1.1288
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Epoch 19/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4948 - loss: 1.1352 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4969 - loss: 1.1301
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4963 - loss: 1.1299
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4950 - loss: 1.1310
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4940 - loss: 1.1317
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4929 - loss: 1.1324
[1m263/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4917 - loss: 1.1332
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4907 - loss: 1.1339
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4902 - loss: 1.1344 - val_accuracy: 0.5460 - val_loss: 1.1214
Epoch 20/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4688 - loss: 0.9654
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4688 - loss: 1.1370 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4780 - loss: 1.1370
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4797 - loss: 1.1375
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4788 - loss: 1.1385
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4784 - loss: 1.1391
[1m226/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4787 - loss: 1.1395
[1m259/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4793 - loss: 1.1395
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4801 - loss: 1.1393
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4804 - loss: 1.1393 - val_accuracy: 0.5495 - val_loss: 1.1140
Epoch 21/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3750 - loss: 1.2254
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4673 - loss: 1.1521 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4709 - loss: 1.1508
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4709 - loss: 1.1510
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4717 - loss: 1.1499
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4725 - loss: 1.1484
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4735 - loss: 1.1467
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4748 - loss: 1.1452
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4759 - loss: 1.1440
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4764 - loss: 1.1435 - val_accuracy: 0.5390 - val_loss: 1.1153
Epoch 22/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.2059
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4761 - loss: 1.1246 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4800 - loss: 1.1272
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4802 - loss: 1.1292
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4802 - loss: 1.1295
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4801 - loss: 1.1315
[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4807 - loss: 1.1322
[1m260/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4814 - loss: 1.1324
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4821 - loss: 1.1320
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4827 - loss: 1.1317 - val_accuracy: 0.5513 - val_loss: 1.1053
Epoch 23/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1052
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5257 - loss: 1.0833 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5189 - loss: 1.1015
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5153 - loss: 1.1091
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5132 - loss: 1.1123
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5120 - loss: 1.1130
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5109 - loss: 1.1136
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5095 - loss: 1.1146
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5079 - loss: 1.1158
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5070 - loss: 1.1165 - val_accuracy: 0.5579 - val_loss: 1.1084
Epoch 24/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4948 - loss: 1.1779 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5004 - loss: 1.1582
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5017 - loss: 1.1489
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5004 - loss: 1.1442
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4990 - loss: 1.1410
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4985 - loss: 1.1383
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4983 - loss: 1.1361
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4984 - loss: 1.1341
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4986 - loss: 1.1331 - val_accuracy: 0.5629 - val_loss: 1.1063
Epoch 25/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.2087
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5054 - loss: 1.1394 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5067 - loss: 1.1237
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5061 - loss: 1.1205
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5053 - loss: 1.1189
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5053 - loss: 1.1178
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5054 - loss: 1.1168
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5052 - loss: 1.1161
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5049 - loss: 1.1160
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5049 - loss: 1.1159 - val_accuracy: 0.5460 - val_loss: 1.1104
Epoch 26/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5938 - loss: 1.0901
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5128 - loss: 1.1246 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5067 - loss: 1.1186
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5056 - loss: 1.1169
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5047 - loss: 1.1157
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5045 - loss: 1.1148
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5048 - loss: 1.1141
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5047 - loss: 1.1140
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5042 - loss: 1.1140
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5039 - loss: 1.1140 - val_accuracy: 0.5632 - val_loss: 1.0977
Epoch 27/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 19ms/step - accuracy: 0.3438 - loss: 1.2280
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4823 - loss: 1.1116 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4905 - loss: 1.1049
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4927 - loss: 1.1056
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4954 - loss: 1.1053
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4972 - loss: 1.1053
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4988 - loss: 1.1054
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4999 - loss: 1.1054
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5006 - loss: 1.1053
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5006 - loss: 1.1054 - val_accuracy: 0.5632 - val_loss: 1.1123
Epoch 28/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 19ms/step - accuracy: 0.3750 - loss: 1.3093
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5078 - loss: 1.1311 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5107 - loss: 1.1147
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5077 - loss: 1.1095
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5051 - loss: 1.1079
[1m199/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5039 - loss: 1.1070
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5036 - loss: 1.1060
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5037 - loss: 1.1058
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5037 - loss: 1.1054
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5037 - loss: 1.1053 - val_accuracy: 0.5671 - val_loss: 1.0868
Epoch 29/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5196 - loss: 1.0827 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5135 - loss: 1.0884
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5125 - loss: 1.0921
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5119 - loss: 1.0951
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5114 - loss: 1.0971
[1m226/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5115 - loss: 1.0980
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5115 - loss: 1.0986
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5116 - loss: 1.0991
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5118 - loss: 1.0992 - val_accuracy: 0.5671 - val_loss: 1.0840
Epoch 30/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5000 - loss: 1.0961
[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5288 - loss: 1.0788 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5258 - loss: 1.0791
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5228 - loss: 1.0817
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5207 - loss: 1.0828
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5201 - loss: 1.0833
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5191 - loss: 1.0846
[1m261/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5178 - loss: 1.0858
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5164 - loss: 1.0870
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5155 - loss: 1.0878 - val_accuracy: 0.5646 - val_loss: 1.1009
Epoch 31/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5049 - loss: 1.1003 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5152 - loss: 1.0930
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5159 - loss: 1.0902
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5158 - loss: 1.0903
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5154 - loss: 1.0912
[1m223/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5145 - loss: 1.0928
[1m261/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5138 - loss: 1.0938
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5135 - loss: 1.0941
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5134 - loss: 1.0941 - val_accuracy: 0.5520 - val_loss: 1.0953
Epoch 32/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.0856
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5111 - loss: 1.0852 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5160 - loss: 1.0845
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5148 - loss: 1.0870
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5154 - loss: 1.0875
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5170 - loss: 1.0867
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5179 - loss: 1.0866
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5188 - loss: 1.0865
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5195 - loss: 1.0861
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5197 - loss: 1.0862 - val_accuracy: 0.5706 - val_loss: 1.0914
Epoch 33/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 1.0753
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4886 - loss: 1.1264 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5021 - loss: 1.1130
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5048 - loss: 1.1070
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5050 - loss: 1.1049
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5056 - loss: 1.1033
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5071 - loss: 1.1014
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5087 - loss: 1.0999
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5102 - loss: 1.0988
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5108 - loss: 1.0981 - val_accuracy: 0.5685 - val_loss: 1.0826
Epoch 34/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5086 - loss: 1.0795 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5129 - loss: 1.0800
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5117 - loss: 1.0826
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5100 - loss: 1.0837
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[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5097 - loss: 1.0840
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5099 - loss: 1.0838
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5100 - loss: 1.0841
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5100 - loss: 1.0842 - val_accuracy: 0.5720 - val_loss: 1.0910
Epoch 35/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1260
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5390 - loss: 1.0583 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5261 - loss: 1.0706
[1m110/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5226 - loss: 1.0729
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5203 - loss: 1.0749
[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5187 - loss: 1.0770
[1m224/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5172 - loss: 1.0787
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5163 - loss: 1.0795
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5163 - loss: 1.0795
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5162 - loss: 1.0796 - val_accuracy: 0.5685 - val_loss: 1.0872
Epoch 36/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5333 - loss: 1.0835 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5344 - loss: 1.0771
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5348 - loss: 1.0741
[1m162/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5341 - loss: 1.0734
[1m200/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5336 - loss: 1.0730
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5327 - loss: 1.0736
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5321 - loss: 1.0737
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5314 - loss: 1.0738
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5312 - loss: 1.0739 - val_accuracy: 0.5639 - val_loss: 1.0757
Epoch 37/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4062 - loss: 1.0214
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4904 - loss: 1.0921 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5074 - loss: 1.0857
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5124 - loss: 1.0843
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5144 - loss: 1.0830
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5148 - loss: 1.0826
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5150 - loss: 1.0823
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5151 - loss: 1.0826
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5153 - loss: 1.0823
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5155 - loss: 1.0821 - val_accuracy: 0.5541 - val_loss: 1.0897
Epoch 38/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0812
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5099 - loss: 1.0823 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5173 - loss: 1.0751
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5217 - loss: 1.0714
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5240 - loss: 1.0703
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5262 - loss: 1.0686
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5279 - loss: 1.0673
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5293 - loss: 1.0659
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5300 - loss: 1.0655
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5302 - loss: 1.0654 - val_accuracy: 0.5737 - val_loss: 1.0783
Epoch 39/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5355 - loss: 1.0520 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5397 - loss: 1.0513
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5392 - loss: 1.0542
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5383 - loss: 1.0556
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[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5361 - loss: 1.0574
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5350 - loss: 1.0587
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5337 - loss: 1.0599
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5330 - loss: 1.0602 - val_accuracy: 0.5632 - val_loss: 1.0698
Epoch 40/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5625 - loss: 1.0953
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5526 - loss: 1.0352 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5482 - loss: 1.0396
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[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5404 - loss: 1.0504
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[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5382 - loss: 1.0542
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5377 - loss: 1.0551
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5374 - loss: 1.0557
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5373 - loss: 1.0560 - val_accuracy: 0.5713 - val_loss: 1.0788
Epoch 41/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5175 - loss: 1.0560 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5234 - loss: 1.0596
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5260 - loss: 1.0595
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5277 - loss: 1.0591
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[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5311 - loss: 1.0572
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5319 - loss: 1.0566
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5323 - loss: 1.0565
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5323 - loss: 1.0566 - val_accuracy: 0.5660 - val_loss: 1.0753
Epoch 42/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5237 - loss: 1.0485 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5211 - loss: 1.0566
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5204 - loss: 1.0572
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5211 - loss: 1.0579
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5222 - loss: 1.0573
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5236 - loss: 1.0567
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5244 - loss: 1.0568
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5253 - loss: 1.0569
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5260 - loss: 1.0570 - val_accuracy: 0.5678 - val_loss: 1.0801
Epoch 43/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6875 - loss: 0.9418
[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5442 - loss: 1.0817 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5353 - loss: 1.0818
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[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5354 - loss: 1.0703
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[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5377 - loss: 1.0633
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5379 - loss: 1.0618
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5378 - loss: 1.0609
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5378 - loss: 1.0604 - val_accuracy: 0.5657 - val_loss: 1.0693
Epoch 44/53

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[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5854 - loss: 0.9982 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5683 - loss: 1.0169
[1m110/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5620 - loss: 1.0241
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5599 - loss: 1.0277
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[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5550 - loss: 1.0333
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5532 - loss: 1.0350
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5523 - loss: 1.0359 - val_accuracy: 0.5650 - val_loss: 1.0785
Epoch 45/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.6250 - loss: 1.0025
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5617 - loss: 1.0388 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5500 - loss: 1.0491
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[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5463 - loss: 1.0471
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[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5452 - loss: 1.0448
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5443 - loss: 1.0447
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5439 - loss: 1.0442
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5438 - loss: 1.0440 - val_accuracy: 0.5692 - val_loss: 1.0895
Epoch 46/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5400 - loss: 1.0160 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5420 - loss: 1.0193
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5409 - loss: 1.0264
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5409 - loss: 1.0304
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5415 - loss: 1.0322
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5419 - loss: 1.0332
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5425 - loss: 1.0343
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5426 - loss: 1.0352
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5427 - loss: 1.0353 - val_accuracy: 0.5801 - val_loss: 1.0810
Epoch 47/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0228
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5315 - loss: 1.0406 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5383 - loss: 1.0341
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5369 - loss: 1.0345
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5360 - loss: 1.0359
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5359 - loss: 1.0374
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5364 - loss: 1.0379
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5369 - loss: 1.0382
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5374 - loss: 1.0388
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5375 - loss: 1.0391 - val_accuracy: 0.5804 - val_loss: 1.0737
Epoch 48/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6562 - loss: 0.8905
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5716 - loss: 1.0146 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5604 - loss: 1.0288
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5539 - loss: 1.0376
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5511 - loss: 1.0405
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5501 - loss: 1.0411
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5498 - loss: 1.0409
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5500 - loss: 1.0406
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Epoch 49/53

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[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5331 - loss: 1.0291 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5403 - loss: 1.0204
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[1m142/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5422 - loss: 1.0246
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[1m258/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5452 - loss: 1.0271
[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5457 - loss: 1.0273
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Epoch 50/53

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[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5324 - loss: 1.0659 
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[1m142/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5509 - loss: 1.0551
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[1m254/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5540 - loss: 1.0471
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Epoch 51/53

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[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5747 - loss: 1.0060
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[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5665 - loss: 1.0085
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[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5609 - loss: 1.0120
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5595 - loss: 1.0131
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5588 - loss: 1.0137 - val_accuracy: 0.5720 - val_loss: 1.0777
Epoch 52/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5490 - loss: 1.0331 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5513 - loss: 1.0285
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5515 - loss: 1.0292
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5521 - loss: 1.0291
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5522 - loss: 1.0293
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5520 - loss: 1.0292
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5519 - loss: 1.0292
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5519 - loss: 1.0292
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5518 - loss: 1.0293 - val_accuracy: 0.5671 - val_loss: 1.0841
Epoch 53/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5667 - loss: 0.9760 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5615 - loss: 0.9891
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Saved model to disk.

Accuracy capturado en la ejecución 15: 51.26 [%]
F1-score capturado en la ejecución 15: 50.17 [%]

=== EJECUCIÓN 16 ===

--- TRAIN (ejecución 16) ---

--- TEST (ejecución 16) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:18[0m 973ms/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m65/89[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 791us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 931us/step
Global accuracy score (validation) = 58.18 [%]
Global F1 score (validation) = 57.17 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.316351   0.4072004  0.12063542 0.1558133 ]
 [0.1615714  0.14069526 0.6515559  0.04617751]
 [0.29574487 0.20159285 0.41961968 0.08304256]
 ...
 [0.18859042 0.16998644 0.57278043 0.06864276]
 [0.19065188 0.14151955 0.602876   0.06495255]
 [0.22007635 0.17110303 0.56034374 0.04847688]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 60.15 [%]
Global accuracy score (test) = 53.07 [%]
Global F1 score (train) = 59.93 [%]
Global F1 score (test) = 52.81 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.43      0.33      0.37       400
MODERATE-INTENSITY       0.49      0.58      0.53       400
         SEDENTARY       0.52      0.72      0.61       400
VIGOROUS-INTENSITY       0.80      0.49      0.61       345

          accuracy                           0.53      1545
         macro avg       0.56      0.53      0.53      1545
      weighted avg       0.55      0.53      0.53      1545

2025-11-05 10:49:09.594273: 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`.
2025-11-05 10:49:09.605690: 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 00:00:1762336149.618921 3050478 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762336149.623031 3050478 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762336149.632770 3050478 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336149.632790 3050478 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336149.632800 3050478 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336149.632802 3050478 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:49:09.636017: 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 00:00:1762336151.850681 3050478 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 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..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/53
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762336153.487274 3050571 service.cc:152] XLA service 0x7a57f0003310 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762336153.487329 3050571 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:49:13.522768: 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 00:00:1762336153.704420 3050571 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762336155.851863 3050571 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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[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2794 - loss: 2.4506  
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2786 - loss: 2.3719
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2808 - loss: 2.2808
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2828 - loss: 2.2018
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2854 - loss: 2.1354
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2877 - loss: 2.0790
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2897 - loss: 2.0283
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2914 - loss: 1.9877
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Epoch 2/53

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[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3048 - loss: 1.3730 
[1m 68/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3138 - loss: 1.3756
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[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3240 - loss: 1.3725
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[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3296 - loss: 1.3695
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3316 - loss: 1.3673
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3335 - loss: 1.3651
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3343 - loss: 1.3643 - val_accuracy: 0.4846 - val_loss: 1.2444
Epoch 3/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3720 - loss: 1.3134 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3699 - loss: 1.3143
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3710 - loss: 1.3123
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3732 - loss: 1.3090
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3751 - loss: 1.3072
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3763 - loss: 1.3066
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3769 - loss: 1.3062
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3774 - loss: 1.3058
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3777 - loss: 1.3055 - val_accuracy: 0.4846 - val_loss: 1.2241
Epoch 4/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3438 - loss: 1.3212
[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3905 - loss: 1.2849 
[1m 70/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3981 - loss: 1.2769
[1m108/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3985 - loss: 1.2752
[1m146/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3991 - loss: 1.2739
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3997 - loss: 1.2737
[1m224/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3999 - loss: 1.2738
[1m263/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3998 - loss: 1.2740
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3999 - loss: 1.2741
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3999 - loss: 1.2741 - val_accuracy: 0.5011 - val_loss: 1.2078
Epoch 5/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.1562 - loss: 1.3998
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3575 - loss: 1.3018 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3768 - loss: 1.2885
[1m109/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3871 - loss: 1.2797
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3929 - loss: 1.2744
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3971 - loss: 1.2710
[1m226/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4000 - loss: 1.2684
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4027 - loss: 1.2661
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4048 - loss: 1.2642
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4059 - loss: 1.2631 - val_accuracy: 0.5004 - val_loss: 1.1823
Epoch 6/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3935 - loss: 1.2511 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4035 - loss: 1.2430
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4081 - loss: 1.2396
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4126 - loss: 1.2370
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4164 - loss: 1.2358
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4189 - loss: 1.2352
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4209 - loss: 1.2343
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4226 - loss: 1.2337
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4232 - loss: 1.2335 - val_accuracy: 0.4958 - val_loss: 1.1746
Epoch 7/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4379 - loss: 1.2427 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4417 - loss: 1.2332
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4401 - loss: 1.2308
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4387 - loss: 1.2305
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4379 - loss: 1.2299
[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4373 - loss: 1.2299
[1m259/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4369 - loss: 1.2296
[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4367 - loss: 1.2292
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4368 - loss: 1.2287 - val_accuracy: 0.5095 - val_loss: 1.1679
Epoch 8/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4643 - loss: 1.1721 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4619 - loss: 1.1795
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4599 - loss: 1.1859
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4565 - loss: 1.1912
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[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4527 - loss: 1.1967
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4518 - loss: 1.1982
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4512 - loss: 1.1992
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4507 - loss: 1.1998 - val_accuracy: 0.4965 - val_loss: 1.1805
Epoch 9/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1861
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4383 - loss: 1.2084 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4404 - loss: 1.2043
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4420 - loss: 1.2037
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4432 - loss: 1.2031
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4442 - loss: 1.2023
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4446 - loss: 1.2021
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4449 - loss: 1.2020
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4456 - loss: 1.2019
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4458 - loss: 1.2017 - val_accuracy: 0.5116 - val_loss: 1.1581
Epoch 10/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 1.1546
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4934 - loss: 1.1648 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4818 - loss: 1.1767
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4758 - loss: 1.1818
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4719 - loss: 1.1843
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4691 - loss: 1.1855
[1m223/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4671 - loss: 1.1859
[1m258/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4660 - loss: 1.1858
[1m294/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4654 - loss: 1.1855
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4649 - loss: 1.1855 - val_accuracy: 0.5204 - val_loss: 1.1703
Epoch 11/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4361 - loss: 1.2101 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4357 - loss: 1.2048
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4366 - loss: 1.1997
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4381 - loss: 1.1973
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4398 - loss: 1.1960
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4418 - loss: 1.1948
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4439 - loss: 1.1935
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4457 - loss: 1.1922
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4465 - loss: 1.1917 - val_accuracy: 0.5007 - val_loss: 1.1507
Epoch 12/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.3750 - loss: 1.2045
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4423 - loss: 1.1924 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4501 - loss: 1.1873
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4539 - loss: 1.1859
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4564 - loss: 1.1842
[1m200/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4575 - loss: 1.1834
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4586 - loss: 1.1832
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4597 - loss: 1.1827
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4604 - loss: 1.1822
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4606 - loss: 1.1820 - val_accuracy: 0.5025 - val_loss: 1.1472
Epoch 13/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4994 - loss: 1.1266 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4935 - loss: 1.1372
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4876 - loss: 1.1462
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4844 - loss: 1.1503
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4820 - loss: 1.1527
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4800 - loss: 1.1547
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4785 - loss: 1.1558
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4772 - loss: 1.1568
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4764 - loss: 1.1575 - val_accuracy: 0.5274 - val_loss: 1.1436
Epoch 14/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3750 - loss: 1.2781
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4695 - loss: 1.1652 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4688 - loss: 1.1565
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4695 - loss: 1.1541
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4709 - loss: 1.1535
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4712 - loss: 1.1540
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4715 - loss: 1.1547
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4720 - loss: 1.1548
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4728 - loss: 1.1542
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4730 - loss: 1.1540 - val_accuracy: 0.5214 - val_loss: 1.1372
Epoch 15/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0475
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4883 - loss: 1.1564 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4858 - loss: 1.1587
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4827 - loss: 1.1610
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4810 - loss: 1.1609
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4797 - loss: 1.1613
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4784 - loss: 1.1622
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4778 - loss: 1.1624
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4773 - loss: 1.1623
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4773 - loss: 1.1621 - val_accuracy: 0.5435 - val_loss: 1.1438
Epoch 16/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4920 - loss: 1.1737 
[1m 71/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4837 - loss: 1.1626
[1m106/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4785 - loss: 1.1602
[1m146/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4762 - loss: 1.1594
[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4749 - loss: 1.1589
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[1m263/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4756 - loss: 1.1563
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4755 - loss: 1.1555
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4754 - loss: 1.1553 - val_accuracy: 0.5355 - val_loss: 1.1354
Epoch 17/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 1.0595
[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4986 - loss: 1.1449 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4960 - loss: 1.1491
[1m107/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4970 - loss: 1.1488
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4963 - loss: 1.1487
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4951 - loss: 1.1488
[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4938 - loss: 1.1492
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[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4919 - loss: 1.1481
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4915 - loss: 1.1478 - val_accuracy: 0.5320 - val_loss: 1.1194
Epoch 18/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4736 - loss: 1.1461 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4776 - loss: 1.1381
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4808 - loss: 1.1392
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4830 - loss: 1.1399
[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4850 - loss: 1.1396
[1m226/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4861 - loss: 1.1398
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4869 - loss: 1.1393
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4874 - loss: 1.1390
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4877 - loss: 1.1388 - val_accuracy: 0.5383 - val_loss: 1.1308
Epoch 19/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4910 - loss: 1.1060 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4934 - loss: 1.1158
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4937 - loss: 1.1214
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4937 - loss: 1.1242
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4936 - loss: 1.1254
[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4935 - loss: 1.1265
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4931 - loss: 1.1277
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4927 - loss: 1.1287
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4924 - loss: 1.1294 - val_accuracy: 0.5442 - val_loss: 1.1421
Epoch 20/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 1.0320
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5068 - loss: 1.1409 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5004 - loss: 1.1374
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4983 - loss: 1.1342
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4963 - loss: 1.1340
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[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4941 - loss: 1.1335
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4930 - loss: 1.1334
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4921 - loss: 1.1336
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4917 - loss: 1.1337 - val_accuracy: 0.5386 - val_loss: 1.1234
Epoch 21/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4949 - loss: 1.1281 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4990 - loss: 1.1261
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4979 - loss: 1.1265
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4965 - loss: 1.1286
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4960 - loss: 1.1284
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4957 - loss: 1.1282
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4956 - loss: 1.1279
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4954 - loss: 1.1274
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4953 - loss: 1.1271 - val_accuracy: 0.5650 - val_loss: 1.1237
Epoch 22/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5625 - loss: 1.1170
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4987 - loss: 1.1453 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4944 - loss: 1.1402
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4966 - loss: 1.1329
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4981 - loss: 1.1297
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4989 - loss: 1.1277
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4995 - loss: 1.1260
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4997 - loss: 1.1245
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4997 - loss: 1.1236
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4996 - loss: 1.1233 - val_accuracy: 0.5411 - val_loss: 1.1300

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 410ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 17ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 17ms/step
Saved model to disk.

Accuracy capturado en la ejecución 16: 53.07 [%]
F1-score capturado en la ejecución 16: 52.81 [%]

=== EJECUCIÓN 17 ===

--- TRAIN (ejecución 17) ---

--- TEST (ejecución 17) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:25[0m 997ms/step
[1m 65/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 786us/step  
[1m139/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 732us/step
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[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 734us/step
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 15ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m67/89[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 766us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 883us/step
Global accuracy score (validation) = 53.09 [%]
Global F1 score (validation) = 52.16 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.07672358 0.09405518 0.8198585  0.00936286]
 [0.17565851 0.12030894 0.63497585 0.06905677]
 [0.17565851 0.12030894 0.63497585 0.06905677]
 ...
 [0.2338095  0.1376014  0.504625   0.12396404]
 [0.2511125  0.1471468  0.406681   0.19505969]
 [0.19285503 0.12100013 0.625374   0.06077088]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 52.51 [%]
Global accuracy score (test) = 48.54 [%]
Global F1 score (train) = 52.73 [%]
Global F1 score (test) = 48.02 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.36      0.28      0.31       400
MODERATE-INTENSITY       0.44      0.48      0.46       400
         SEDENTARY       0.48      0.74      0.59       400
VIGOROUS-INTENSITY       0.79      0.43      0.56       345

          accuracy                           0.49      1545
         macro avg       0.52      0.48      0.48      1545
      weighted avg       0.51      0.49      0.48      1545

2025-11-05 10:49:42.415023: 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`.
2025-11-05 10:49:42.427013: 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 00:00:1762336182.441261 3053481 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762336182.445691 3053481 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762336182.455950 3053481 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336182.455972 3053481 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336182.455975 3053481 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336182.455977 3053481 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:49:42.459181: 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 00:00:1762336184.685825 3053481 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 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..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/53
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762336186.283916 3053612 service.cc:152] XLA service 0x755c84004b50 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762336186.283945 3053612 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:49:46.316305: 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 00:00:1762336186.484058 3053612 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762336188.692535 3053612 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 2/53

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Epoch 3/53

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Epoch 4/53

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[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3882 - loss: 1.2884
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[1m283/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3902 - loss: 1.2863
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Epoch 5/53

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[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4013 - loss: 1.2623
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Epoch 6/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4412 - loss: 1.2351 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4366 - loss: 1.2340
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[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4310 - loss: 1.2372
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[1m263/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4277 - loss: 1.2411
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4270 - loss: 1.2416
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4268 - loss: 1.2418 - val_accuracy: 0.5151 - val_loss: 1.1753
Epoch 7/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4272 - loss: 1.2360 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4233 - loss: 1.2358
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4235 - loss: 1.2334
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4237 - loss: 1.2334
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4246 - loss: 1.2328
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4258 - loss: 1.2316
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4271 - loss: 1.2303
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4287 - loss: 1.2288
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4293 - loss: 1.2283 - val_accuracy: 0.5183 - val_loss: 1.1634
Epoch 8/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4403 - loss: 1.2576 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4336 - loss: 1.2497
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[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4352 - loss: 1.2354
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4354 - loss: 1.2314
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4358 - loss: 1.2286
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4360 - loss: 1.2265
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4364 - loss: 1.2246
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Epoch 9/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4760 - loss: 1.1882 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4651 - loss: 1.1920
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4601 - loss: 1.1933
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4575 - loss: 1.1941
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4548 - loss: 1.1956
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[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4526 - loss: 1.1973
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Epoch 10/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4585 - loss: 1.1581 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4605 - loss: 1.1639
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[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4551 - loss: 1.1799
[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4538 - loss: 1.1826
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Epoch 11/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4437 - loss: 1.1841 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4512 - loss: 1.1846
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4524 - loss: 1.1819
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4533 - loss: 1.1818
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4534 - loss: 1.1835
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4534 - loss: 1.1848
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4535 - loss: 1.1856
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4536 - loss: 1.1858
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4536 - loss: 1.1859 - val_accuracy: 0.5270 - val_loss: 1.1390
Epoch 12/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3125 - loss: 1.3160
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4292 - loss: 1.1991 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4338 - loss: 1.1960
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4393 - loss: 1.1920
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4451 - loss: 1.1877
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4494 - loss: 1.1847
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4523 - loss: 1.1824
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4545 - loss: 1.1802
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4558 - loss: 1.1789
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4562 - loss: 1.1786 - val_accuracy: 0.5316 - val_loss: 1.1392
Epoch 13/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 19ms/step - accuracy: 0.5312 - loss: 1.0325
[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4811 - loss: 1.1256 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4767 - loss: 1.1439
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4726 - loss: 1.1516
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4710 - loss: 1.1546
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4693 - loss: 1.1582
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4681 - loss: 1.1610
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4674 - loss: 1.1624
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4667 - loss: 1.1636
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4662 - loss: 1.1644 - val_accuracy: 0.5158 - val_loss: 1.1262
Epoch 14/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5194 - loss: 1.1243 
[1m 83/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5073 - loss: 1.1364
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5022 - loss: 1.1393
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4979 - loss: 1.1417
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4949 - loss: 1.1439
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4927 - loss: 1.1453
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4905 - loss: 1.1467
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4884 - loss: 1.1483
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4876 - loss: 1.1490 - val_accuracy: 0.5274 - val_loss: 1.1387
Epoch 15/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4519 - loss: 1.1856 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4621 - loss: 1.1685
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4666 - loss: 1.1638
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4673 - loss: 1.1625
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4678 - loss: 1.1611
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4689 - loss: 1.1595
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4694 - loss: 1.1593
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4699 - loss: 1.1594 - val_accuracy: 0.5334 - val_loss: 1.1172
Epoch 16/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4795 - loss: 1.1688 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4773 - loss: 1.1636
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4766 - loss: 1.1606
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4769 - loss: 1.1586
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4767 - loss: 1.1573
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4766 - loss: 1.1569
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4768 - loss: 1.1561
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4770 - loss: 1.1555
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4769 - loss: 1.1553 - val_accuracy: 0.5583 - val_loss: 1.1359
Epoch 17/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.4375 - loss: 1.1711
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4460 - loss: 1.1589 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4608 - loss: 1.1514
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4653 - loss: 1.1497
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4673 - loss: 1.1502
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4686 - loss: 1.1511
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4696 - loss: 1.1519
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4707 - loss: 1.1520
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4716 - loss: 1.1516
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4720 - loss: 1.1514 - val_accuracy: 0.5281 - val_loss: 1.1216
Epoch 18/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1852
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4610 - loss: 1.1465 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4644 - loss: 1.1486
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4685 - loss: 1.1488
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4723 - loss: 1.1472
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[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4742 - loss: 1.1470
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4746 - loss: 1.1466
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4751 - loss: 1.1461
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4752 - loss: 1.1460 - val_accuracy: 0.5513 - val_loss: 1.1110
Epoch 19/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5049 - loss: 1.1054 
[1m 71/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4975 - loss: 1.1163
[1m105/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4937 - loss: 1.1206
[1m145/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4901 - loss: 1.1246
[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4876 - loss: 1.1272
[1m223/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4856 - loss: 1.1295
[1m261/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4839 - loss: 1.1315
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4830 - loss: 1.1329
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4827 - loss: 1.1336 - val_accuracy: 0.5365 - val_loss: 1.1027
Epoch 20/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4062 - loss: 1.1651
[1m 42/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4812 - loss: 1.1320 
[1m 82/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4867 - loss: 1.1262
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4880 - loss: 1.1250
[1m161/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4878 - loss: 1.1272
[1m202/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4880 - loss: 1.1291
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4885 - loss: 1.1296
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4894 - loss: 1.1297
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4902 - loss: 1.1297
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4905 - loss: 1.1296 - val_accuracy: 0.5632 - val_loss: 1.1118
Epoch 21/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5008 - loss: 1.1099 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5017 - loss: 1.1129
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4984 - loss: 1.1188
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4957 - loss: 1.1220
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4948 - loss: 1.1235
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4938 - loss: 1.1249
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4930 - loss: 1.1258
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4925 - loss: 1.1263
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4922 - loss: 1.1266 - val_accuracy: 0.5625 - val_loss: 1.1140
Epoch 22/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.5625 - loss: 1.0401
[1m 42/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5023 - loss: 1.0974 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5012 - loss: 1.1023
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5000 - loss: 1.1060
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4997 - loss: 1.1075
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4999 - loss: 1.1082
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5001 - loss: 1.1092
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5002 - loss: 1.1100
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5001 - loss: 1.1106
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4999 - loss: 1.1109 - val_accuracy: 0.5442 - val_loss: 1.1073
Epoch 23/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 1.1136
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4883 - loss: 1.1441 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4950 - loss: 1.1269
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4962 - loss: 1.1239
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4970 - loss: 1.1219
[1m200/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4964 - loss: 1.1217
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4960 - loss: 1.1217
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4957 - loss: 1.1211
[1m321/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4954 - loss: 1.1207
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4954 - loss: 1.1206 - val_accuracy: 0.5643 - val_loss: 1.0939
Epoch 24/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4786 - loss: 1.1601 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4946 - loss: 1.1341
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5005 - loss: 1.1242
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5023 - loss: 1.1205
[1m202/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5021 - loss: 1.1197
[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5012 - loss: 1.1196
[1m281/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5003 - loss: 1.1194
[1m320/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4996 - loss: 1.1190
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4995 - loss: 1.1188 - val_accuracy: 0.5432 - val_loss: 1.1063
Epoch 25/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.2176
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4921 - loss: 1.1369 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4939 - loss: 1.1282
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4935 - loss: 1.1242
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4934 - loss: 1.1208
[1m200/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4937 - loss: 1.1176
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4942 - loss: 1.1160
[1m281/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4946 - loss: 1.1147
[1m322/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4946 - loss: 1.1140
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4947 - loss: 1.1139 - val_accuracy: 0.5671 - val_loss: 1.0970
Epoch 26/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 0.9884
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5219 - loss: 1.0806 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5162 - loss: 1.0918
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5131 - loss: 1.1002
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5134 - loss: 1.1026
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5137 - loss: 1.1040
[1m219/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5137 - loss: 1.1050
[1m254/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5135 - loss: 1.1059
[1m292/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5133 - loss: 1.1061
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5130 - loss: 1.1061 - val_accuracy: 0.5607 - val_loss: 1.0913
Epoch 27/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.3750 - loss: 1.0982
[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5011 - loss: 1.0790 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5063 - loss: 1.0864
[1m108/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5065 - loss: 1.0874
[1m146/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5072 - loss: 1.0865
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5074 - loss: 1.0868
[1m221/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5070 - loss: 1.0882
[1m260/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5061 - loss: 1.0903
[1m297/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5052 - loss: 1.0920
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5045 - loss: 1.0932 - val_accuracy: 0.5744 - val_loss: 1.1033
Epoch 28/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4062 - loss: 1.1714
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4949 - loss: 1.0944 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4995 - loss: 1.0964
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5022 - loss: 1.0976
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5037 - loss: 1.0986
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5049 - loss: 1.0990
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5056 - loss: 1.0988
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5061 - loss: 1.0988
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5064 - loss: 1.0992
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5065 - loss: 1.0994 - val_accuracy: 0.5509 - val_loss: 1.1056
Epoch 29/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5088 - loss: 1.0974 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5089 - loss: 1.0913
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5098 - loss: 1.0883
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5099 - loss: 1.0893
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5092 - loss: 1.0901
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5089 - loss: 1.0909
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5089 - loss: 1.0911
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5090 - loss: 1.0910
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5091 - loss: 1.0910 - val_accuracy: 0.5678 - val_loss: 1.1025
Epoch 30/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 0.9900
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5100 - loss: 1.0849 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5049 - loss: 1.0885
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5057 - loss: 1.0901
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5068 - loss: 1.0904
[1m199/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5072 - loss: 1.0899
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5078 - loss: 1.0893
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5080 - loss: 1.0891
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5081 - loss: 1.0890
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5082 - loss: 1.0890 - val_accuracy: 0.5804 - val_loss: 1.0940
Epoch 31/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.1706
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5182 - loss: 1.0819 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5217 - loss: 1.0805
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[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5248 - loss: 1.0783
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5250 - loss: 1.0783
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5250 - loss: 1.0787
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5249 - loss: 1.0790 - val_accuracy: 0.5660 - val_loss: 1.0963

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 407ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 16ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 16ms/step
Saved model to disk.

Accuracy capturado en la ejecución 17: 48.54 [%]
F1-score capturado en la ejecución 17: 48.02 [%]

=== EJECUCIÓN 18 ===

--- TRAIN (ejecución 18) ---

--- TEST (ejecución 18) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:32[0m 1s/step
[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 864us/step
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 17ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 15ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m60/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 847us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 935us/step
Global accuracy score (validation) = 56.14 [%]
Global F1 score (validation) = 54.06 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.344365   0.57435817 0.07152878 0.00974801]
 [0.17217286 0.14567116 0.62930965 0.05284626]
 [0.11154253 0.11847299 0.74942553 0.02055892]
 ...
 [0.2607917  0.15625282 0.45252004 0.13043547]
 [0.23823777 0.13366862 0.47816843 0.1499253 ]
 [0.1922799  0.13649236 0.60497284 0.06625491]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 55.57 [%]
Global accuracy score (test) = 51.33 [%]
Global F1 score (train) = 55.17 [%]
Global F1 score (test) = 50.57 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.38      0.24      0.29       400
MODERATE-INTENSITY       0.48      0.64      0.55       400
         SEDENTARY       0.51      0.67      0.58       400
VIGOROUS-INTENSITY       0.75      0.51      0.60       345

          accuracy                           0.51      1545
         macro avg       0.53      0.51      0.51      1545
      weighted avg       0.52      0.51      0.50      1545

2025-11-05 10:50:20.479728: 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`.
2025-11-05 10:50:20.491187: 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 00:00:1762336220.504617 3057340 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762336220.508703 3057340 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762336220.518875 3057340 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336220.518897 3057340 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336220.518906 3057340 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336220.518908 3057340 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:50:20.522181: 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 00:00:1762336222.751963 3057340 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 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..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/53
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762336224.347635 3057457 service.cc:152] XLA service 0x7b780c002810 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762336224.347666 3057457 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:50:24.381551: 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 00:00:1762336224.552524 3057457 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762336226.693081 3057457 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18:06[0m 3s/step - accuracy: 0.2812 - loss: 2.5278
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2659 - loss: 2.5454  
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2739 - loss: 2.3952
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2790 - loss: 2.2771
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2816 - loss: 2.1896
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2840 - loss: 2.1145
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2865 - loss: 2.0527
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2892 - loss: 2.0000
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2915 - loss: 1.9565
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step - accuracy: 0.2923 - loss: 1.9428
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 10ms/step - accuracy: 0.2923 - loss: 1.9418 - val_accuracy: 0.4751 - val_loss: 1.2647
Epoch 2/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3438 - loss: 1.4384
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3235 - loss: 1.3907 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3269 - loss: 1.3839
[1m110/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3270 - loss: 1.3818
[1m146/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3272 - loss: 1.3807
[1m179/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3286 - loss: 1.3789
[1m220/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3306 - loss: 1.3762
[1m257/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3317 - loss: 1.3741
[1m296/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3328 - loss: 1.3723
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3339 - loss: 1.3706 - val_accuracy: 0.4853 - val_loss: 1.2389
Epoch 3/53

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[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3615 - loss: 1.3259
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[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3645 - loss: 1.3240
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[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3676 - loss: 1.3209
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Epoch 4/53

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[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3735 - loss: 1.3007
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[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3823 - loss: 1.2911
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[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3886 - loss: 1.2876
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3899 - loss: 1.2865
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Epoch 5/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3848 - loss: 1.2903 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3998 - loss: 1.2718
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[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4044 - loss: 1.2647
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[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4076 - loss: 1.2609
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4091 - loss: 1.2595
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4103 - loss: 1.2582
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4107 - loss: 1.2577 - val_accuracy: 0.4996 - val_loss: 1.1735
Epoch 6/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4346 - loss: 1.2346 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4263 - loss: 1.2439
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[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4230 - loss: 1.2482
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4224 - loss: 1.2479
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4223 - loss: 1.2472
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4229 - loss: 1.2458
[1m319/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4235 - loss: 1.2444
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4236 - loss: 1.2441 - val_accuracy: 0.5137 - val_loss: 1.1653
Epoch 7/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1953
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4311 - loss: 1.2293 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4291 - loss: 1.2266
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4293 - loss: 1.2264
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4304 - loss: 1.2253
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4317 - loss: 1.2236
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[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4330 - loss: 1.2203
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4336 - loss: 1.2188
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4338 - loss: 1.2182 - val_accuracy: 0.5144 - val_loss: 1.1601
Epoch 8/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4727 - loss: 1.2263 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4579 - loss: 1.2190
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4513 - loss: 1.2160
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4500 - loss: 1.2129
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4503 - loss: 1.2107
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4502 - loss: 1.2087
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4499 - loss: 1.2075
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4496 - loss: 1.2068
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4494 - loss: 1.2066 - val_accuracy: 0.4947 - val_loss: 1.1666
Epoch 9/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3750 - loss: 1.1491
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4281 - loss: 1.1993 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4351 - loss: 1.1994
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4370 - loss: 1.1991
[1m161/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4378 - loss: 1.1986
[1m201/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4391 - loss: 1.1980
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4406 - loss: 1.1974
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4418 - loss: 1.1967
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4428 - loss: 1.1961
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4432 - loss: 1.1958 - val_accuracy: 0.5133 - val_loss: 1.1565
Epoch 10/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3438 - loss: 1.2962
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4324 - loss: 1.1901 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4380 - loss: 1.1938
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4399 - loss: 1.1972
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4416 - loss: 1.1987
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4424 - loss: 1.1994
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4435 - loss: 1.1991
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4443 - loss: 1.1989
[1m317/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4451 - loss: 1.1985
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4453 - loss: 1.1984 - val_accuracy: 0.5372 - val_loss: 1.1413
Epoch 11/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.2042
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4514 - loss: 1.2040 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4539 - loss: 1.1956
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4559 - loss: 1.1901
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4570 - loss: 1.1872
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4570 - loss: 1.1861
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4577 - loss: 1.1849
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4579 - loss: 1.1841
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4581 - loss: 1.1834
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4583 - loss: 1.1829 - val_accuracy: 0.5302 - val_loss: 1.1565
Epoch 12/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.1099
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4720 - loss: 1.1407 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4763 - loss: 1.1384
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4757 - loss: 1.1405
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4748 - loss: 1.1448
[1m201/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4739 - loss: 1.1488
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4731 - loss: 1.1516
[1m281/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4723 - loss: 1.1538
[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4717 - loss: 1.1553
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4716 - loss: 1.1558 - val_accuracy: 0.5302 - val_loss: 1.1339
Epoch 13/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4839 - loss: 1.1247 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4797 - loss: 1.1390
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4792 - loss: 1.1446
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4788 - loss: 1.1477
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4784 - loss: 1.1488
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4782 - loss: 1.1494
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4781 - loss: 1.1504
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4777 - loss: 1.1514
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4774 - loss: 1.1517 - val_accuracy: 0.5488 - val_loss: 1.1320
Epoch 14/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.0854
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4663 - loss: 1.1401 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4625 - loss: 1.1545
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4649 - loss: 1.1571
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4668 - loss: 1.1588
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4683 - loss: 1.1594
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4692 - loss: 1.1596
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4695 - loss: 1.1596
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4695 - loss: 1.1596
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4695 - loss: 1.1596 - val_accuracy: 0.5481 - val_loss: 1.1253
Epoch 15/53

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[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4659 - loss: 1.1684 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4772 - loss: 1.1604
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4791 - loss: 1.1568
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4788 - loss: 1.1556
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4781 - loss: 1.1552
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4779 - loss: 1.1548
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4777 - loss: 1.1547
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4773 - loss: 1.1549
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4771 - loss: 1.1550 - val_accuracy: 0.5456 - val_loss: 1.1187
Epoch 16/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.2155
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4819 - loss: 1.1435 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4898 - loss: 1.1359
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4877 - loss: 1.1366
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4854 - loss: 1.1387
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4835 - loss: 1.1412
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4820 - loss: 1.1433
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4813 - loss: 1.1445
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4811 - loss: 1.1450
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4810 - loss: 1.1451 - val_accuracy: 0.5537 - val_loss: 1.1204
Epoch 17/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 0.9181
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4923 - loss: 1.1206 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4906 - loss: 1.1250
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4879 - loss: 1.1281
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4860 - loss: 1.1296
[1m199/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4853 - loss: 1.1297
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4850 - loss: 1.1303
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4846 - loss: 1.1311
[1m317/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4843 - loss: 1.1315
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4842 - loss: 1.1317 - val_accuracy: 0.5414 - val_loss: 1.1404
Epoch 18/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4909 - loss: 1.1398 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4802 - loss: 1.1453
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4762 - loss: 1.1497
[1m161/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4768 - loss: 1.1488
[1m202/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4782 - loss: 1.1470
[1m243/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4799 - loss: 1.1450
[1m284/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4812 - loss: 1.1431
[1m320/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4818 - loss: 1.1421
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4820 - loss: 1.1419 - val_accuracy: 0.5520 - val_loss: 1.1215
Epoch 19/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.1666
[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5050 - loss: 1.1206 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5022 - loss: 1.1211
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5004 - loss: 1.1213
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4996 - loss: 1.1212
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4988 - loss: 1.1215
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4981 - loss: 1.1217
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4977 - loss: 1.1221
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4971 - loss: 1.1226
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4967 - loss: 1.1229 - val_accuracy: 0.5460 - val_loss: 1.1144
Epoch 20/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1396
[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5155 - loss: 1.1009 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5133 - loss: 1.1060
[1m106/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5079 - loss: 1.1112
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5039 - loss: 1.1126
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5021 - loss: 1.1129
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5005 - loss: 1.1139
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4998 - loss: 1.1143
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4992 - loss: 1.1147
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4988 - loss: 1.1150 - val_accuracy: 0.5298 - val_loss: 1.1212
Epoch 21/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1125
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4982 - loss: 1.1132 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5004 - loss: 1.1173
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5011 - loss: 1.1179
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5013 - loss: 1.1178
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5014 - loss: 1.1175
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5012 - loss: 1.1172
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5012 - loss: 1.1169
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5008 - loss: 1.1171
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5006 - loss: 1.1173 - val_accuracy: 0.5520 - val_loss: 1.1298
Epoch 22/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4062 - loss: 1.2228
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4459 - loss: 1.1616 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4651 - loss: 1.1394
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4694 - loss: 1.1351
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4731 - loss: 1.1316
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4769 - loss: 1.1279
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4798 - loss: 1.1250
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4824 - loss: 1.1226
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4846 - loss: 1.1212
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4852 - loss: 1.1209 - val_accuracy: 0.5383 - val_loss: 1.1269
Epoch 23/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4761 - loss: 1.1357 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4781 - loss: 1.1375
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4827 - loss: 1.1358
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4871 - loss: 1.1315
[1m199/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4895 - loss: 1.1288
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4904 - loss: 1.1273
[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4913 - loss: 1.1260
[1m319/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4922 - loss: 1.1245
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4924 - loss: 1.1241 - val_accuracy: 0.5720 - val_loss: 1.1066
Epoch 24/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 0.9494
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5178 - loss: 1.0790 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5108 - loss: 1.0942
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5088 - loss: 1.1022
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5066 - loss: 1.1070
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5052 - loss: 1.1087
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5043 - loss: 1.1095
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5042 - loss: 1.1097
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5040 - loss: 1.1100
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5040 - loss: 1.1100 - val_accuracy: 0.5639 - val_loss: 1.1036
Epoch 25/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5044 - loss: 1.1073 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5069 - loss: 1.1070
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5081 - loss: 1.1029
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5093 - loss: 1.1003
[1m201/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5096 - loss: 1.0992
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5095 - loss: 1.0986
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5095 - loss: 1.0985
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5092 - loss: 1.0987
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5091 - loss: 1.0989 - val_accuracy: 0.5727 - val_loss: 1.1115
Epoch 26/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.3750 - loss: 1.2503
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4592 - loss: 1.1500 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4744 - loss: 1.1362
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4827 - loss: 1.1286
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4877 - loss: 1.1225
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4915 - loss: 1.1184
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4938 - loss: 1.1161
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4957 - loss: 1.1139
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4969 - loss: 1.1126
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4975 - loss: 1.1120 - val_accuracy: 0.5723 - val_loss: 1.1102
Epoch 27/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.1226
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5269 - loss: 1.0554 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5200 - loss: 1.0652
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5175 - loss: 1.0678
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5171 - loss: 1.0686
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5171 - loss: 1.0703
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5165 - loss: 1.0728
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5161 - loss: 1.0749
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5156 - loss: 1.0768
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5153 - loss: 1.0778 - val_accuracy: 0.5632 - val_loss: 1.1047
Epoch 28/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5076 - loss: 1.0621 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5132 - loss: 1.0679
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5150 - loss: 1.0713
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5166 - loss: 1.0725
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5164 - loss: 1.0754
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5162 - loss: 1.0778
[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5166 - loss: 1.0795
[1m319/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5169 - loss: 1.0801
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5169 - loss: 1.0803 - val_accuracy: 0.5674 - val_loss: 1.1012
Epoch 29/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6250 - loss: 0.8578
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5048 - loss: 1.0749 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5130 - loss: 1.0765
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5122 - loss: 1.0808
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5115 - loss: 1.0844
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5107 - loss: 1.0877
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5101 - loss: 1.0891
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5102 - loss: 1.0894
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5103 - loss: 1.0896
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5105 - loss: 1.0894 - val_accuracy: 0.5583 - val_loss: 1.0829
Epoch 30/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5354 - loss: 1.0606 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5315 - loss: 1.0697
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5258 - loss: 1.0759
[1m162/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5222 - loss: 1.0796
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[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5209 - loss: 1.0818
[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5207 - loss: 1.0820
[1m317/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5206 - loss: 1.0821
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5205 - loss: 1.0822 - val_accuracy: 0.5730 - val_loss: 1.0938
Epoch 31/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 1.0251
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5089 - loss: 1.1076 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5117 - loss: 1.0947
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5120 - loss: 1.0900
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5116 - loss: 1.0875
[1m200/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5116 - loss: 1.0866
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5117 - loss: 1.0860
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5116 - loss: 1.0859
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5115 - loss: 1.0857
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5115 - loss: 1.0857 - val_accuracy: 0.5636 - val_loss: 1.0983
Epoch 32/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.5625 - loss: 1.2573
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5313 - loss: 1.0823 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5224 - loss: 1.0709
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5213 - loss: 1.0688
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5217 - loss: 1.0677
[1m201/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5218 - loss: 1.0682
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5214 - loss: 1.0691
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5212 - loss: 1.0699
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5211 - loss: 1.0704
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5211 - loss: 1.0705 - val_accuracy: 0.5836 - val_loss: 1.1017
Epoch 33/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5182 - loss: 1.0672 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5201 - loss: 1.0680
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5226 - loss: 1.0682
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5247 - loss: 1.0679
[1m201/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5264 - loss: 1.0681
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5266 - loss: 1.0695
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5262 - loss: 1.0709
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5260 - loss: 1.0716
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Epoch 34/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 27ms/step - accuracy: 0.5312 - loss: 1.0167
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[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5099 - loss: 1.0759
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[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5141 - loss: 1.0785
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[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5156 - loss: 1.0782
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 407ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 16ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 16ms/step
Saved model to disk.

Accuracy capturado en la ejecución 18: 51.33 [%]
F1-score capturado en la ejecución 18: 50.57 [%]

=== EJECUCIÓN 19 ===

--- TRAIN (ejecución 19) ---

--- TEST (ejecución 19) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:17[0m 970ms/step
[1m 71/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 723us/step  
[1m143/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 711us/step
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[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 727us/step
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 15ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m64/89[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 796us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 929us/step
Global accuracy score (validation) = 55.83 [%]
Global F1 score (validation) = 55.27 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.31757942 0.21081953 0.41732836 0.05427269]
 [0.20345744 0.16065656 0.5745985  0.06128752]
 [0.26419473 0.17211924 0.4824629  0.08122302]
 ...
 [0.20946781 0.1668736  0.5524094  0.07124905]
 [0.22994511 0.13529803 0.5238684  0.11088849]
 [0.29745233 0.21938843 0.3985221  0.0846371 ]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 57.44 [%]
Global accuracy score (test) = 51.72 [%]
Global F1 score (train) = 57.54 [%]
Global F1 score (test) = 51.59 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.40      0.30      0.34       400
MODERATE-INTENSITY       0.48      0.65      0.55       400
         SEDENTARY       0.52      0.62      0.56       400
VIGOROUS-INTENSITY       0.78      0.50      0.61       345

          accuracy                           0.52      1545
         macro avg       0.54      0.52      0.52      1545
      weighted avg       0.53      0.52      0.51      1545

2025-11-05 10:51:00.186527: 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`.
2025-11-05 10:51:00.197889: 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 00:00:1762336260.211337 3061456 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762336260.215693 3061456 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762336260.225681 3061456 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336260.225708 3061456 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336260.225711 3061456 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336260.225712 3061456 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:51:00.228898: 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 00:00:1762336262.472909 3061456 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 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..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/53
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762336264.091041 3061586 service.cc:152] XLA service 0x7984b400c3a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762336264.091084 3061586 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:51:04.126054: 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 00:00:1762336264.295857 3061586 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762336266.467339 3061586 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 2/53

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Epoch 3/53

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Epoch 4/53

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[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3972 - loss: 1.2810
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[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3978 - loss: 1.2807
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Epoch 5/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3995 - loss: 1.2656 
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[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4098 - loss: 1.2629
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[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4120 - loss: 1.2627
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4120 - loss: 1.2624
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Epoch 6/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4132 - loss: 1.2203 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4207 - loss: 1.2318
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[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4271 - loss: 1.2314
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[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4285 - loss: 1.2321
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4289 - loss: 1.2322
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4290 - loss: 1.2322
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4291 - loss: 1.2322 - val_accuracy: 0.5376 - val_loss: 1.1614
Epoch 7/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4329 - loss: 1.2288 
[1m 70/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4311 - loss: 1.2242
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4312 - loss: 1.2244
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4320 - loss: 1.2241
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4327 - loss: 1.2231
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4334 - loss: 1.2221
[1m263/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4342 - loss: 1.2208
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4347 - loss: 1.2199
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4350 - loss: 1.2195 - val_accuracy: 0.5151 - val_loss: 1.1440
Epoch 8/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.2612
[1m 42/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4302 - loss: 1.2204 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4340 - loss: 1.2184
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[1m161/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4400 - loss: 1.2134
[1m202/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4416 - loss: 1.2106
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4422 - loss: 1.2092
[1m281/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4428 - loss: 1.2080
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4429 - loss: 1.2075
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Epoch 9/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4485 - loss: 1.1748 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4455 - loss: 1.1843
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4473 - loss: 1.1897
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4495 - loss: 1.1928
[1m199/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4509 - loss: 1.1946
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4511 - loss: 1.1958
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4510 - loss: 1.1967
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4511 - loss: 1.1971
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4513 - loss: 1.1971 - val_accuracy: 0.5165 - val_loss: 1.1311
Epoch 10/53

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4716 - loss: 1.1865 
[1m 67/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4603 - loss: 1.1930
[1m104/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4590 - loss: 1.1909
[1m141/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4585 - loss: 1.1894
[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4591 - loss: 1.1875
[1m220/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4593 - loss: 1.1861
[1m257/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4594 - loss: 1.1853
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4592 - loss: 1.1851
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4592 - loss: 1.1850 - val_accuracy: 0.5291 - val_loss: 1.1384
Epoch 11/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.2883
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4039 - loss: 1.2335 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4178 - loss: 1.2163
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4258 - loss: 1.2079
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4329 - loss: 1.2009
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4377 - loss: 1.1970
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4413 - loss: 1.1944
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4441 - loss: 1.1925
[1m319/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4460 - loss: 1.1913
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4463 - loss: 1.1912 - val_accuracy: 0.5471 - val_loss: 1.1284
Epoch 12/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.3750 - loss: 1.4043
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4215 - loss: 1.1948 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4356 - loss: 1.1897
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4423 - loss: 1.1881
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4456 - loss: 1.1876
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4476 - loss: 1.1868
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4489 - loss: 1.1862
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4499 - loss: 1.1858
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4508 - loss: 1.1851
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4515 - loss: 1.1844 - val_accuracy: 0.5449 - val_loss: 1.1160
Epoch 13/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.2212
[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5099 - loss: 1.1452 
[1m 70/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5011 - loss: 1.1441
[1m109/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4961 - loss: 1.1461
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4916 - loss: 1.1499
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4893 - loss: 1.1521
[1m222/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4879 - loss: 1.1530
[1m258/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4867 - loss: 1.1540
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4852 - loss: 1.1556
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4842 - loss: 1.1564 - val_accuracy: 0.5449 - val_loss: 1.1149
Epoch 14/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4887 - loss: 1.1603 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4733 - loss: 1.1652
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4689 - loss: 1.1666
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4665 - loss: 1.1670
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4655 - loss: 1.1669
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4651 - loss: 1.1664
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4656 - loss: 1.1656
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4661 - loss: 1.1644
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4664 - loss: 1.1640 - val_accuracy: 0.5305 - val_loss: 1.1305
Epoch 15/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4375 - loss: 1.1088
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4505 - loss: 1.1597 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4523 - loss: 1.1598
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4515 - loss: 1.1625
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4523 - loss: 1.1624
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4538 - loss: 1.1617
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4552 - loss: 1.1611
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4565 - loss: 1.1607
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4577 - loss: 1.1603
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4581 - loss: 1.1601 - val_accuracy: 0.5442 - val_loss: 1.1106
Epoch 16/53

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[1m 42/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4505 - loss: 1.1653 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4603 - loss: 1.1553
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4668 - loss: 1.1521
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4709 - loss: 1.1513
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4733 - loss: 1.1510
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4750 - loss: 1.1511
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4763 - loss: 1.1508
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4770 - loss: 1.1508
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4775 - loss: 1.1508 - val_accuracy: 0.5453 - val_loss: 1.1068
Epoch 17/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3750 - loss: 1.2178
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4461 - loss: 1.1610 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4540 - loss: 1.1594
[1m107/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4593 - loss: 1.1581
[1m146/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4633 - loss: 1.1567
[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4656 - loss: 1.1554
[1m216/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4678 - loss: 1.1545
[1m254/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4697 - loss: 1.1532
[1m293/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4713 - loss: 1.1521
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4725 - loss: 1.1511 - val_accuracy: 0.5492 - val_loss: 1.1006
Epoch 18/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1961
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4691 - loss: 1.1495 
[1m 71/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4719 - loss: 1.1482
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4741 - loss: 1.1494
[1m146/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4758 - loss: 1.1498
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4776 - loss: 1.1495
[1m223/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4790 - loss: 1.1489
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4799 - loss: 1.1482
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4806 - loss: 1.1480
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4808 - loss: 1.1477 - val_accuracy: 0.5439 - val_loss: 1.1020
Epoch 19/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4713 - loss: 1.1285 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4668 - loss: 1.1336
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4684 - loss: 1.1333
[1m162/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4706 - loss: 1.1339
[1m202/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4722 - loss: 1.1346
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4736 - loss: 1.1353
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4749 - loss: 1.1355
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4762 - loss: 1.1354
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4766 - loss: 1.1353 - val_accuracy: 0.5572 - val_loss: 1.1003
Epoch 20/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5312 - loss: 1.1717
[1m 43/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5186 - loss: 1.0877 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5097 - loss: 1.1064
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5048 - loss: 1.1160
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5011 - loss: 1.1212
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4989 - loss: 1.1249
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4977 - loss: 1.1268
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4965 - loss: 1.1283
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4958 - loss: 1.1293
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4955 - loss: 1.1297 - val_accuracy: 0.5667 - val_loss: 1.0905
Epoch 21/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.2212
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5138 - loss: 1.1361 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5079 - loss: 1.1340
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5027 - loss: 1.1346
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5011 - loss: 1.1339
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5011 - loss: 1.1310
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5011 - loss: 1.1293
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5005 - loss: 1.1286
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4997 - loss: 1.1284
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4994 - loss: 1.1284 - val_accuracy: 0.5425 - val_loss: 1.0985
Epoch 22/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 0.9731
[1m 42/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4834 - loss: 1.1281 
[1m 82/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4866 - loss: 1.1224
[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4894 - loss: 1.1230
[1m165/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4919 - loss: 1.1229
[1m203/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4927 - loss: 1.1227
[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4928 - loss: 1.1235
[1m283/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4929 - loss: 1.1239
[1m322/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4934 - loss: 1.1237
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4935 - loss: 1.1237 - val_accuracy: 0.5674 - val_loss: 1.0908
Epoch 23/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 1.1099
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5111 - loss: 1.1073 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5030 - loss: 1.1077
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5019 - loss: 1.1059
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5015 - loss: 1.1059
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5005 - loss: 1.1068
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4998 - loss: 1.1073
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4995 - loss: 1.1081
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4993 - loss: 1.1092
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4992 - loss: 1.1098 - val_accuracy: 0.5699 - val_loss: 1.0931
Epoch 24/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4754 - loss: 1.1210 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4773 - loss: 1.1256
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4770 - loss: 1.1278
[1m162/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4792 - loss: 1.1257
[1m203/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4813 - loss: 1.1240
[1m243/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4826 - loss: 1.1234
[1m283/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4838 - loss: 1.1226
[1m320/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4852 - loss: 1.1213
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4855 - loss: 1.1211 - val_accuracy: 0.5667 - val_loss: 1.0879
Epoch 25/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.4062 - loss: 1.1108
[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5184 - loss: 1.1220 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5155 - loss: 1.1135
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5151 - loss: 1.1088
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5147 - loss: 1.1061
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5146 - loss: 1.1045
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5141 - loss: 1.1039
[1m263/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5130 - loss: 1.1042
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5119 - loss: 1.1045
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5115 - loss: 1.1046 - val_accuracy: 0.5681 - val_loss: 1.0835
Epoch 26/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 26ms/step - accuracy: 0.5000 - loss: 1.3349
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5117 - loss: 1.1536 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5069 - loss: 1.1445
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5048 - loss: 1.1379
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5043 - loss: 1.1333
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5037 - loss: 1.1307
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5033 - loss: 1.1285
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5029 - loss: 1.1266
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5026 - loss: 1.1250
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5026 - loss: 1.1244 - val_accuracy: 0.5709 - val_loss: 1.0842
Epoch 27/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9624
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5223 - loss: 1.0858 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5145 - loss: 1.0911
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5113 - loss: 1.0968
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5075 - loss: 1.1024
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5054 - loss: 1.1049
[1m224/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5047 - loss: 1.1055
[1m261/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5048 - loss: 1.1049
[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5051 - loss: 1.1044
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5055 - loss: 1.1040 - val_accuracy: 0.5660 - val_loss: 1.0810
Epoch 28/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6250 - loss: 1.0657
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5511 - loss: 1.0693 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5386 - loss: 1.0717
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5302 - loss: 1.0767
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5255 - loss: 1.0809
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5224 - loss: 1.0843
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5207 - loss: 1.0868
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5198 - loss: 1.0883
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5191 - loss: 1.0895
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5188 - loss: 1.0899 - val_accuracy: 0.5720 - val_loss: 1.0821
Epoch 29/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5069 - loss: 1.0978 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5102 - loss: 1.0937
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5114 - loss: 1.0926
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5136 - loss: 1.0915
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5149 - loss: 1.0903
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5156 - loss: 1.0899
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5161 - loss: 1.0899
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5165 - loss: 1.0899
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5168 - loss: 1.0899 - val_accuracy: 0.5660 - val_loss: 1.0838
Epoch 30/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 1.0828
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5126 - loss: 1.0767 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5088 - loss: 1.0817
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5089 - loss: 1.0833
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5085 - loss: 1.0854
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5088 - loss: 1.0869
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5095 - loss: 1.0873
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5097 - loss: 1.0877
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5098 - loss: 1.0883
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5100 - loss: 1.0884 - val_accuracy: 0.5776 - val_loss: 1.0835
Epoch 31/53

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[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5422 - loss: 1.0433 
[1m 71/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5361 - loss: 1.0566
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5285 - loss: 1.0671
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5243 - loss: 1.0731
[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5228 - loss: 1.0762
[1m224/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5222 - loss: 1.0770
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5218 - loss: 1.0778
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5212 - loss: 1.0786
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5210 - loss: 1.0788 - val_accuracy: 0.5650 - val_loss: 1.0781
Epoch 32/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5625 - loss: 1.0453
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5464 - loss: 1.0462 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5460 - loss: 1.0478
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5473 - loss: 1.0473
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5449 - loss: 1.0517
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5421 - loss: 1.0560
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5400 - loss: 1.0591
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5380 - loss: 1.0621
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5364 - loss: 1.0646
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5358 - loss: 1.0655 - val_accuracy: 0.5779 - val_loss: 1.0670
Epoch 33/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6875 - loss: 0.9631
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5404 - loss: 1.0465 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5323 - loss: 1.0549
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5282 - loss: 1.0623
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5274 - loss: 1.0657
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5254 - loss: 1.0688
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5242 - loss: 1.0713
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5237 - loss: 1.0731
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5233 - loss: 1.0744
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5233 - loss: 1.0747 - val_accuracy: 0.5860 - val_loss: 1.0672
Epoch 34/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5276 - loss: 1.0800 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5278 - loss: 1.0766
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5293 - loss: 1.0743
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5309 - loss: 1.0713
[1m201/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5312 - loss: 1.0696
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5313 - loss: 1.0689
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5312 - loss: 1.0684
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5309 - loss: 1.0683
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5307 - loss: 1.0684 - val_accuracy: 0.5579 - val_loss: 1.0736
Epoch 35/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.6250 - loss: 0.9394
[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5540 - loss: 1.0462 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5380 - loss: 1.0601
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5346 - loss: 1.0631
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5332 - loss: 1.0648
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[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5314 - loss: 1.0668
[1m263/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5309 - loss: 1.0670
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5306 - loss: 1.0671
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5306 - loss: 1.0670 - val_accuracy: 0.5702 - val_loss: 1.0753
Epoch 36/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.5625 - loss: 0.9927
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5121 - loss: 1.0882 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5185 - loss: 1.0755
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5176 - loss: 1.0748
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5173 - loss: 1.0737
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5176 - loss: 1.0729
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5184 - loss: 1.0724
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5190 - loss: 1.0725
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5198 - loss: 1.0724
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5200 - loss: 1.0723 - val_accuracy: 0.5762 - val_loss: 1.0705
Epoch 37/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6250 - loss: 0.9913
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5522 - loss: 1.0370 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5432 - loss: 1.0454
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[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5405 - loss: 1.0514
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[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5379 - loss: 1.0559
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5378 - loss: 1.0566
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5379 - loss: 1.0567 - val_accuracy: 0.5751 - val_loss: 1.0825

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 394ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 16ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 16ms/step
Saved model to disk.

Accuracy capturado en la ejecución 19: 51.72 [%]
F1-score capturado en la ejecución 19: 51.59 [%]

=== EJECUCIÓN 20 ===

--- TRAIN (ejecución 20) ---

--- TEST (ejecución 20) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m67/89[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 758us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 872us/step
Global accuracy score (validation) = 56.95 [%]
Global F1 score (validation) = 55.01 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.12905005 0.09993169 0.7189629  0.05205525]
 [0.20291695 0.20724055 0.55202293 0.03781954]
 [0.12382662 0.09709007 0.73030746 0.04877584]
 ...
 [0.22014788 0.1544722  0.5344402  0.09093974]
 [0.24492644 0.1363731  0.49836183 0.12033853]
 [0.17922257 0.14489128 0.6169612  0.05892503]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 57.13 [%]
Global accuracy score (test) = 51.91 [%]
Global F1 score (train) = 56.6 [%]
Global F1 score (test) = 51.61 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.37      0.26      0.30       400
MODERATE-INTENSITY       0.46      0.64      0.53       400
         SEDENTARY       0.56      0.65      0.60       400
VIGOROUS-INTENSITY       0.77      0.53      0.63       345

          accuracy                           0.52      1545
         macro avg       0.54      0.52      0.52      1545
      weighted avg       0.53      0.52      0.51      1545

2025-11-05 10:51:41.802490: 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`.
2025-11-05 10:51:41.813788: 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 00:00:1762336301.827063 3065891 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762336301.831251 3065891 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762336301.841159 3065891 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336301.841177 3065891 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336301.841180 3065891 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336301.841181 3065891 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:51:41.844319: 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 00:00:1762336304.066226 3065891 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 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..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/53
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762336305.719779 3066002 service.cc:152] XLA service 0x7fc3c401df30 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762336305.719813 3066002 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:51:45.752807: 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 00:00:1762336305.926425 3066002 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762336308.072612 3066002 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18:24[0m 3s/step - accuracy: 0.1562 - loss: 3.4554
[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2606 - loss: 2.7186  
[1m 71/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2708 - loss: 2.5258
[1m109/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2761 - loss: 2.4009
[1m146/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2806 - loss: 2.3026
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2842 - loss: 2.2184
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2872 - loss: 2.1455
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2896 - loss: 2.0860
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2916 - loss: 2.0381
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step - accuracy: 0.2924 - loss: 2.0179
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 10ms/step - accuracy: 0.2925 - loss: 2.0169 - val_accuracy: 0.4628 - val_loss: 1.2630
Epoch 2/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3438 - loss: 1.3824
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[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3207 - loss: 1.3994
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[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3233 - loss: 1.3914
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Epoch 3/53

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[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3700 - loss: 1.3258
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[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3716 - loss: 1.3202
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Epoch 4/53

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[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4053 - loss: 1.2806 
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[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4002 - loss: 1.2793
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[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3989 - loss: 1.2800
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3985 - loss: 1.2805
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3983 - loss: 1.2806
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3983 - loss: 1.2806 - val_accuracy: 0.4881 - val_loss: 1.2238
Epoch 5/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3895 - loss: 1.2716 
[1m 82/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3969 - loss: 1.2726
[1m123/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4053 - loss: 1.2686
[1m163/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4097 - loss: 1.2658
[1m201/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4122 - loss: 1.2641
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4138 - loss: 1.2630
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4147 - loss: 1.2621
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4154 - loss: 1.2613
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4158 - loss: 1.2608 - val_accuracy: 0.5074 - val_loss: 1.1927
Epoch 6/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4106 - loss: 1.2574 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4159 - loss: 1.2535
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[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4146 - loss: 1.2550
[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4156 - loss: 1.2534
[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4165 - loss: 1.2515
[1m259/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4176 - loss: 1.2497
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4186 - loss: 1.2485
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Epoch 7/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4569 - loss: 1.1902 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4556 - loss: 1.1999
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4519 - loss: 1.2072
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4488 - loss: 1.2124
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4475 - loss: 1.2141
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4467 - loss: 1.2152
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4461 - loss: 1.2162
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Epoch 8/53

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[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4131 - loss: 1.2385 
[1m 68/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4237 - loss: 1.2314
[1m108/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4305 - loss: 1.2237
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4332 - loss: 1.2208
[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4348 - loss: 1.2189
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[1m261/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4367 - loss: 1.2164
[1m297/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4372 - loss: 1.2155
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Epoch 9/53

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[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4513 - loss: 1.2190 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4452 - loss: 1.2135
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4441 - loss: 1.2120
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4442 - loss: 1.2112
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4446 - loss: 1.2108
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4452 - loss: 1.2097
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4458 - loss: 1.2082
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4463 - loss: 1.2071
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4467 - loss: 1.2063 - val_accuracy: 0.5323 - val_loss: 1.1447
Epoch 10/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3750 - loss: 1.1487
[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4593 - loss: 1.1819 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4632 - loss: 1.1800
[1m108/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4668 - loss: 1.1750
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4686 - loss: 1.1733
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4687 - loss: 1.1725
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4684 - loss: 1.1729
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4684 - loss: 1.1736
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4681 - loss: 1.1745
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4678 - loss: 1.1751 - val_accuracy: 0.5204 - val_loss: 1.1460
Epoch 11/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3438 - loss: 1.2868
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4608 - loss: 1.1813 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4582 - loss: 1.1882
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4571 - loss: 1.1911
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4575 - loss: 1.1905
[1m201/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4587 - loss: 1.1890
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4596 - loss: 1.1878
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4600 - loss: 1.1868
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4600 - loss: 1.1862
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4599 - loss: 1.1861 - val_accuracy: 0.5137 - val_loss: 1.1460
Epoch 12/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4579 - loss: 1.1513 
[1m 71/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4643 - loss: 1.1553
[1m108/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4690 - loss: 1.1567
[1m145/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4726 - loss: 1.1568
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4740 - loss: 1.1588
[1m221/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4739 - loss: 1.1611
[1m261/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4732 - loss: 1.1636
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4721 - loss: 1.1658
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4716 - loss: 1.1666 - val_accuracy: 0.5302 - val_loss: 1.1336
Epoch 13/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1888
[1m 43/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4611 - loss: 1.1709 
[1m 84/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4649 - loss: 1.1727
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4675 - loss: 1.1720
[1m163/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4693 - loss: 1.1704
[1m203/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4697 - loss: 1.1695
[1m245/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4699 - loss: 1.1694
[1m286/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4700 - loss: 1.1696
[1m326/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4702 - loss: 1.1697
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4702 - loss: 1.1696 - val_accuracy: 0.5235 - val_loss: 1.1436
Epoch 14/53

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[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4688 - loss: 1.1684 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4713 - loss: 1.1662
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4722 - loss: 1.1645
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4727 - loss: 1.1642
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4728 - loss: 1.1642
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4732 - loss: 1.1637
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4738 - loss: 1.1631
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4743 - loss: 1.1624
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4744 - loss: 1.1622 - val_accuracy: 0.5112 - val_loss: 1.1229
Epoch 15/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1983
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4483 - loss: 1.1686 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4594 - loss: 1.1669
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4661 - loss: 1.1611
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4697 - loss: 1.1567
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4723 - loss: 1.1548
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4739 - loss: 1.1539
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4753 - loss: 1.1529
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4762 - loss: 1.1522
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4764 - loss: 1.1521 - val_accuracy: 0.5126 - val_loss: 1.1240
Epoch 16/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.6562 - loss: 0.9113
[1m 42/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4848 - loss: 1.1221 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4763 - loss: 1.1379
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4722 - loss: 1.1472
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4705 - loss: 1.1515
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4705 - loss: 1.1530
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4708 - loss: 1.1537
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4715 - loss: 1.1536
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4720 - loss: 1.1537
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4723 - loss: 1.1537 - val_accuracy: 0.5270 - val_loss: 1.1262
Epoch 17/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4439 - loss: 1.1736 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4563 - loss: 1.1670
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4649 - loss: 1.1631
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4687 - loss: 1.1620
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4718 - loss: 1.1601
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4743 - loss: 1.1580
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4763 - loss: 1.1562
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4773 - loss: 1.1557
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4776 - loss: 1.1555 - val_accuracy: 0.5393 - val_loss: 1.1135
Epoch 18/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2812 - loss: 1.4088
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4669 - loss: 1.1745 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4740 - loss: 1.1673
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4770 - loss: 1.1625
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4794 - loss: 1.1581
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4809 - loss: 1.1551
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4816 - loss: 1.1524
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4822 - loss: 1.1505
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4828 - loss: 1.1490
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4832 - loss: 1.1484 - val_accuracy: 0.5386 - val_loss: 1.1305
Epoch 19/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4837 - loss: 1.1403 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4862 - loss: 1.1413
[1m110/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4859 - loss: 1.1406
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4859 - loss: 1.1400
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4857 - loss: 1.1392
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4859 - loss: 1.1386
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4860 - loss: 1.1382
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4863 - loss: 1.1376
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4865 - loss: 1.1372 - val_accuracy: 0.5520 - val_loss: 1.1093
Epoch 20/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3438 - loss: 1.3238
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4780 - loss: 1.1731 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4834 - loss: 1.1552
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4855 - loss: 1.1490
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4882 - loss: 1.1435
[1m200/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4901 - loss: 1.1400
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4915 - loss: 1.1378
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4923 - loss: 1.1364
[1m320/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4927 - loss: 1.1352
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4927 - loss: 1.1350 - val_accuracy: 0.5534 - val_loss: 1.1073
Epoch 21/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5938 - loss: 1.0908
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4978 - loss: 1.1110 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4957 - loss: 1.1093
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4955 - loss: 1.1116
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4951 - loss: 1.1140
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4950 - loss: 1.1146
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4950 - loss: 1.1145
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4954 - loss: 1.1146
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4952 - loss: 1.1157
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4952 - loss: 1.1161 - val_accuracy: 0.5386 - val_loss: 1.1156
Epoch 22/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4874 - loss: 1.1443 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4910 - loss: 1.1377
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4930 - loss: 1.1349
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4931 - loss: 1.1331
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4929 - loss: 1.1314
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4934 - loss: 1.1298
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4938 - loss: 1.1283
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4942 - loss: 1.1273
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4944 - loss: 1.1268 - val_accuracy: 0.5393 - val_loss: 1.1000
Epoch 23/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.5625 - loss: 1.1795
[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5165 - loss: 1.1211 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5057 - loss: 1.1142
[1m110/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5034 - loss: 1.1104
[1m146/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5020 - loss: 1.1103
[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5007 - loss: 1.1119
[1m224/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5003 - loss: 1.1132
[1m263/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5001 - loss: 1.1141
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4999 - loss: 1.1148
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4999 - loss: 1.1151 - val_accuracy: 0.5614 - val_loss: 1.0949
Epoch 24/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1646
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5062 - loss: 1.1136 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5056 - loss: 1.1163
[1m109/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5049 - loss: 1.1167
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5037 - loss: 1.1170
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5024 - loss: 1.1175
[1m226/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5017 - loss: 1.1180
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5014 - loss: 1.1186
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5011 - loss: 1.1186
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5011 - loss: 1.1184 - val_accuracy: 0.5527 - val_loss: 1.1116
Epoch 25/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.1593
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4661 - loss: 1.1417 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4765 - loss: 1.1354
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4860 - loss: 1.1270
[1m161/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4909 - loss: 1.1227
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4934 - loss: 1.1201
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4950 - loss: 1.1181
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4957 - loss: 1.1170
[1m319/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4963 - loss: 1.1162
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4965 - loss: 1.1160 - val_accuracy: 0.5439 - val_loss: 1.1042
Epoch 26/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0913
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5366 - loss: 1.0853 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5269 - loss: 1.0925
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5205 - loss: 1.0976
[1m162/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5177 - loss: 1.0992
[1m200/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5167 - loss: 1.0989
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5161 - loss: 1.0983
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5156 - loss: 1.0978
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5148 - loss: 1.0976
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5145 - loss: 1.0977 - val_accuracy: 0.5516 - val_loss: 1.1140
Epoch 27/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4782 - loss: 1.1379 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4890 - loss: 1.1233
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4933 - loss: 1.1196
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4963 - loss: 1.1166
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4979 - loss: 1.1149
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4991 - loss: 1.1135
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5001 - loss: 1.1121
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5011 - loss: 1.1107
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5015 - loss: 1.1100 - val_accuracy: 0.5727 - val_loss: 1.0874
Epoch 28/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 19ms/step - accuracy: 0.4688 - loss: 1.1446
[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5331 - loss: 1.0403 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5210 - loss: 1.0575
[1m108/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5178 - loss: 1.0621
[1m146/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5170 - loss: 1.0644
[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5161 - loss: 1.0669
[1m222/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5158 - loss: 1.0689
[1m259/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5156 - loss: 1.0710
[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5148 - loss: 1.0738
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5143 - loss: 1.0756 - val_accuracy: 0.5611 - val_loss: 1.0942
Epoch 29/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5378 - loss: 1.0722 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5352 - loss: 1.0740
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5306 - loss: 1.0780
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5273 - loss: 1.0797
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5256 - loss: 1.0806
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5243 - loss: 1.0816
[1m262/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5232 - loss: 1.0823
[1m297/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5224 - loss: 1.0831
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5217 - loss: 1.0838 - val_accuracy: 0.5688 - val_loss: 1.0861
Epoch 30/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 1.1467
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5063 - loss: 1.0977 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5157 - loss: 1.0878
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5216 - loss: 1.0799
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5242 - loss: 1.0768
[1m199/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5248 - loss: 1.0750
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5243 - loss: 1.0755
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5241 - loss: 1.0759
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5238 - loss: 1.0765
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5237 - loss: 1.0767 - val_accuracy: 0.5699 - val_loss: 1.0894
Epoch 31/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6562 - loss: 0.8519
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5514 - loss: 1.0318 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5389 - loss: 1.0460
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5324 - loss: 1.0561
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5274 - loss: 1.0632
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5254 - loss: 1.0666
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5244 - loss: 1.0689
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5236 - loss: 1.0703
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5223 - loss: 1.0720
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5217 - loss: 1.0729 - val_accuracy: 0.5478 - val_loss: 1.0937
Epoch 32/53

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[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4996 - loss: 1.1120 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5084 - loss: 1.0989
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5130 - loss: 1.0921
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5146 - loss: 1.0893
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5149 - loss: 1.0882
[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5153 - loss: 1.0870
[1m259/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5159 - loss: 1.0863
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5167 - loss: 1.0852
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5173 - loss: 1.0846 - val_accuracy: 0.5636 - val_loss: 1.0879
Epoch 33/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0325
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5208 - loss: 1.0602 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5227 - loss: 1.0671
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5230 - loss: 1.0693
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5213 - loss: 1.0724
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5209 - loss: 1.0746
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5211 - loss: 1.0758
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5211 - loss: 1.0764
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5211 - loss: 1.0765
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5211 - loss: 1.0765 - val_accuracy: 0.5822 - val_loss: 1.1015
Epoch 34/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5100 - loss: 1.0960 
[1m 82/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5129 - loss: 1.0893
[1m123/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5159 - loss: 1.0837
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5173 - loss: 1.0813
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5177 - loss: 1.0800
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5186 - loss: 1.0787
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5193 - loss: 1.0780
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5199 - loss: 1.0774
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5201 - loss: 1.0772 - val_accuracy: 0.5769 - val_loss: 1.0751
Epoch 35/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4062 - loss: 1.1256
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5503 - loss: 1.0314 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5395 - loss: 1.0443
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5377 - loss: 1.0480
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5379 - loss: 1.0500
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5377 - loss: 1.0524
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5369 - loss: 1.0549
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5361 - loss: 1.0571
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5356 - loss: 1.0586
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5354 - loss: 1.0591 - val_accuracy: 0.5769 - val_loss: 1.0857
Epoch 36/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3750 - loss: 1.2397
[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5119 - loss: 1.0930 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5260 - loss: 1.0768
[1m107/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5284 - loss: 1.0721
[1m146/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5291 - loss: 1.0694
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5299 - loss: 1.0678
[1m221/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5305 - loss: 1.0670
[1m261/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5310 - loss: 1.0664
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5309 - loss: 1.0663
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5307 - loss: 1.0662 - val_accuracy: 0.5579 - val_loss: 1.0839
Epoch 37/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5190 - loss: 1.0816 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5278 - loss: 1.0684
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5275 - loss: 1.0642
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5258 - loss: 1.0643
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[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5259 - loss: 1.0650
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5262 - loss: 1.0650
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5263 - loss: 1.0651
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Epoch 38/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5625 - loss: 0.9106
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5298 - loss: 1.0622 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5327 - loss: 1.0617
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5338 - loss: 1.0618
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5338 - loss: 1.0611
[1m201/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5335 - loss: 1.0610
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5335 - loss: 1.0607
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5337 - loss: 1.0605
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5338 - loss: 1.0603
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Epoch 39/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.7188 - loss: 0.7550
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 403ms/step
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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 16ms/step
Saved model to disk.

Accuracy capturado en la ejecución 20: 51.91 [%]
F1-score capturado en la ejecución 20: 51.61 [%]

=== EJECUCIÓN 21 ===

--- TRAIN (ejecución 21) ---

--- TEST (ejecución 21) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 15ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m75/89[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 680us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 849us/step
Global accuracy score (validation) = 58.36 [%]
Global F1 score (validation) = 57.38 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.25370508 0.21855414 0.5043038  0.02343699]
 [0.35477987 0.29198453 0.24838246 0.10485318]
 [0.17631665 0.2428204  0.5633595  0.01750347]
 ...
 [0.2521916  0.22383454 0.4293916  0.09458221]
 [0.24440502 0.14101274 0.48429793 0.1302842 ]
 [0.14222226 0.13057275 0.69918436 0.02802056]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 57.21 [%]
Global accuracy score (test) = 52.3 [%]
Global F1 score (train) = 57.12 [%]
Global F1 score (test) = 52.09 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.38      0.28      0.32       400
MODERATE-INTENSITY       0.47      0.60      0.53       400
         SEDENTARY       0.55      0.67      0.60       400
VIGOROUS-INTENSITY       0.76      0.54      0.63       345

          accuracy                           0.52      1545
         macro avg       0.54      0.52      0.52      1545
      weighted avg       0.53      0.52      0.52      1545

2025-11-05 10:52:24.699250: 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`.
2025-11-05 10:52:24.710430: 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 00:00:1762336344.723862 3070495 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762336344.728033 3070495 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762336344.737947 3070495 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336344.737968 3070495 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336344.737970 3070495 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336344.737971 3070495 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:52:24.741074: 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 00:00:1762336346.991832 3070495 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 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..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/53
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762336348.663126 3070625 service.cc:152] XLA service 0x79e6ec004fd0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762336348.663183 3070625 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:52:28.711203: 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 00:00:1762336348.889775 3070625 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762336351.090602 3070625 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18:46[0m 3s/step - accuracy: 0.1875 - loss: 2.9052
[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2357 - loss: 2.5395  
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2547 - loss: 2.3931
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2645 - loss: 2.2881
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2704 - loss: 2.2045
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2742 - loss: 2.1341
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2772 - loss: 2.0750
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2800 - loss: 2.0278
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2829 - loss: 1.9835
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step - accuracy: 0.2840 - loss: 1.9659
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 10ms/step - accuracy: 0.2841 - loss: 1.9650 - val_accuracy: 0.4673 - val_loss: 1.2643
Epoch 2/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2500 - loss: 1.5721
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3148 - loss: 1.4101 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3245 - loss: 1.3970
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3299 - loss: 1.3906
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3330 - loss: 1.3855
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3350 - loss: 1.3814
[1m223/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3362 - loss: 1.3785
[1m263/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3379 - loss: 1.3751
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3398 - loss: 1.3719
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3410 - loss: 1.3700 - val_accuracy: 0.4838 - val_loss: 1.2308
Epoch 3/53

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[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3630 - loss: 1.3360
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3655 - loss: 1.3306
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3678 - loss: 1.3266
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[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3736 - loss: 1.3177
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Epoch 4/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4049 - loss: 1.2865 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3995 - loss: 1.2895
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[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3942 - loss: 1.2914
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[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3957 - loss: 1.2886
[1m262/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3966 - loss: 1.2874
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3975 - loss: 1.2859
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Epoch 5/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4025 - loss: 1.2783 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4057 - loss: 1.2719
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[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4077 - loss: 1.2634
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[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4091 - loss: 1.2593
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4095 - loss: 1.2584
[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4100 - loss: 1.2578
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4101 - loss: 1.2576 - val_accuracy: 0.5200 - val_loss: 1.1731
Epoch 6/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1458
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4264 - loss: 1.2355 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4290 - loss: 1.2401
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4305 - loss: 1.2397
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4301 - loss: 1.2406
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4289 - loss: 1.2407
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4276 - loss: 1.2407
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4272 - loss: 1.2404
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4271 - loss: 1.2400
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4272 - loss: 1.2397 - val_accuracy: 0.5140 - val_loss: 1.1646
Epoch 7/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5000 - loss: 1.3115
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4488 - loss: 1.2331 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4463 - loss: 1.2210
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4439 - loss: 1.2168
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4426 - loss: 1.2146
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4411 - loss: 1.2144
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4403 - loss: 1.2144
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4402 - loss: 1.2143
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4403 - loss: 1.2138
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4404 - loss: 1.2137 - val_accuracy: 0.5263 - val_loss: 1.1491
Epoch 8/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4161 - loss: 1.2503 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4286 - loss: 1.2290
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4317 - loss: 1.2242
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4339 - loss: 1.2215
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4356 - loss: 1.2197
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4362 - loss: 1.2183
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4363 - loss: 1.2172
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4365 - loss: 1.2159
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4365 - loss: 1.2153 - val_accuracy: 0.5116 - val_loss: 1.1720
Epoch 9/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4116 - loss: 1.2256 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4189 - loss: 1.2200
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[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4318 - loss: 1.2075
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4359 - loss: 1.2043
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4387 - loss: 1.2020
[1m260/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4402 - loss: 1.2009
[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4415 - loss: 1.1998
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4423 - loss: 1.1990 - val_accuracy: 0.5246 - val_loss: 1.1445
Epoch 10/53

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[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4587 - loss: 1.1961 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4632 - loss: 1.1797
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4671 - loss: 1.1718
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4679 - loss: 1.1719
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[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4671 - loss: 1.1737
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4665 - loss: 1.1748
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4660 - loss: 1.1754
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4658 - loss: 1.1756 - val_accuracy: 0.5341 - val_loss: 1.1406
Epoch 11/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4665 - loss: 1.1539 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4658 - loss: 1.1624
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4629 - loss: 1.1691
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4600 - loss: 1.1724
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4587 - loss: 1.1735
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4581 - loss: 1.1739
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4580 - loss: 1.1743
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4582 - loss: 1.1745
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4585 - loss: 1.1742 - val_accuracy: 0.5428 - val_loss: 1.1327
Epoch 12/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3750 - loss: 1.3682
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4658 - loss: 1.1901 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4626 - loss: 1.1869
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4590 - loss: 1.1878
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4580 - loss: 1.1876
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4588 - loss: 1.1858
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4590 - loss: 1.1847
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4593 - loss: 1.1837
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4597 - loss: 1.1829
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4598 - loss: 1.1826 - val_accuracy: 0.5407 - val_loss: 1.1398
Epoch 13/53

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[1m 42/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4974 - loss: 1.1322 
[1m 82/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4903 - loss: 1.1393
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4878 - loss: 1.1418
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4870 - loss: 1.1437
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4857 - loss: 1.1463
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4844 - loss: 1.1486
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4835 - loss: 1.1501
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4827 - loss: 1.1513
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4823 - loss: 1.1520 - val_accuracy: 0.5330 - val_loss: 1.1197
Epoch 14/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.5312 - loss: 1.1025
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4627 - loss: 1.1609 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4646 - loss: 1.1609
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4663 - loss: 1.1612
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4677 - loss: 1.1621
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4683 - loss: 1.1634
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4685 - loss: 1.1638
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4685 - loss: 1.1637
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4686 - loss: 1.1633
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4687 - loss: 1.1630 - val_accuracy: 0.5407 - val_loss: 1.1337
Epoch 15/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4886 - loss: 1.1714 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4788 - loss: 1.1748
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4776 - loss: 1.1709
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4773 - loss: 1.1692
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4773 - loss: 1.1672
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4772 - loss: 1.1660
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4772 - loss: 1.1650
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4772 - loss: 1.1639
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4772 - loss: 1.1635 - val_accuracy: 0.5400 - val_loss: 1.1508
Epoch 16/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.0748
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4635 - loss: 1.1615 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4699 - loss: 1.1519
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4713 - loss: 1.1504
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4735 - loss: 1.1475
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4755 - loss: 1.1451
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4765 - loss: 1.1438
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4770 - loss: 1.1430
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4774 - loss: 1.1427
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4774 - loss: 1.1427 - val_accuracy: 0.5425 - val_loss: 1.1294
Epoch 17/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.0305
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4825 - loss: 1.1425 
[1m 82/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4782 - loss: 1.1492
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4762 - loss: 1.1520
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4758 - loss: 1.1523
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4763 - loss: 1.1512
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4771 - loss: 1.1498
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4775 - loss: 1.1490
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4779 - loss: 1.1484
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4781 - loss: 1.1482 - val_accuracy: 0.5562 - val_loss: 1.1321
Epoch 18/53

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m27s[0m 568ms/step
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Saved model to disk.

Accuracy capturado en la ejecución 21: 52.3 [%]
F1-score capturado en la ejecución 21: 52.09 [%]

=== EJECUCIÓN 22 ===

--- TRAIN (ejecución 22) ---

--- TEST (ejecución 22) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:24[0m 991ms/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m66/89[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 771us/step
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Global accuracy score (validation) = 51.26 [%]
Global F1 score (validation) = 49.45 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.29936233 0.2997839  0.3502669  0.05058686]
 [0.26425964 0.2653328  0.41851637 0.05189119]
 [0.0657158  0.06748023 0.8581898  0.00861412]
 ...
 [0.1497746  0.12586345 0.6487081  0.07565386]
 [0.2140733  0.1484641  0.4705313  0.16693118]
 [0.16973715 0.14473294 0.6269268  0.05860313]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 51.38 [%]
Global accuracy score (test) = 46.86 [%]
Global F1 score (train) = 50.75 [%]
Global F1 score (test) = 45.86 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.34      0.17      0.23       400
MODERATE-INTENSITY       0.41      0.64      0.50       400
         SEDENTARY       0.48      0.58      0.52       400
VIGOROUS-INTENSITY       0.74      0.48      0.58       345

          accuracy                           0.47      1545
         macro avg       0.49      0.47      0.46      1545
      weighted avg       0.48      0.47      0.45      1545

2025-11-05 10:52:55.122096: 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`.
2025-11-05 10:52:55.133786: 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 00:00:1762336375.147353 3073113 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762336375.151768 3073113 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762336375.161871 3073113 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336375.161892 3073113 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336375.161894 3073113 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336375.161896 3073113 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:52:55.165180: 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 00:00:1762336377.435843 3073113 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 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..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/53
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762336379.060649 3073242 service.cc:152] XLA service 0x7b979c00d350 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762336379.060691 3073242 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:52:59.096564: 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 00:00:1762336379.263440 3073242 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762336381.397532 3073242 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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[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2791 - loss: 2.1816
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[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2837 - loss: 2.0632
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2851 - loss: 2.0209
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Epoch 2/53

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[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3084 - loss: 1.4023
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3092 - loss: 1.3986
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3120 - loss: 1.3953
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3151 - loss: 1.3919
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3180 - loss: 1.3884
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3211 - loss: 1.3846
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3238 - loss: 1.3812
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Epoch 3/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3513 - loss: 1.3411 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3586 - loss: 1.3354
[1m110/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3618 - loss: 1.3316
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3637 - loss: 1.3287
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3650 - loss: 1.3267
[1m224/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3669 - loss: 1.3249
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3691 - loss: 1.3226
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3704 - loss: 1.3207
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3711 - loss: 1.3196 - val_accuracy: 0.4930 - val_loss: 1.2220
Epoch 4/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3834 - loss: 1.2957 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3848 - loss: 1.2944
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3879 - loss: 1.2932
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3894 - loss: 1.2916
[1m199/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3921 - loss: 1.2887
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3933 - loss: 1.2871
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3941 - loss: 1.2858
[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3949 - loss: 1.2846
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Epoch 5/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4415 - loss: 1.2306 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4385 - loss: 1.2321
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4361 - loss: 1.2358
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4333 - loss: 1.2385
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[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4268 - loss: 1.2437
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Epoch 6/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4089 - loss: 1.2419 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4177 - loss: 1.2370
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[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4236 - loss: 1.2350
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[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4267 - loss: 1.2354
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4276 - loss: 1.2349
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Epoch 7/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4296 - loss: 1.2289 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4285 - loss: 1.2371
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4284 - loss: 1.2373
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4296 - loss: 1.2368
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4306 - loss: 1.2359
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4313 - loss: 1.2343
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4317 - loss: 1.2333
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4324 - loss: 1.2319
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4328 - loss: 1.2313 - val_accuracy: 0.5235 - val_loss: 1.1645
Epoch 8/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.6250 - loss: 0.9912
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4624 - loss: 1.1930 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4552 - loss: 1.1945
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4512 - loss: 1.1978
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4494 - loss: 1.2012
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4481 - loss: 1.2036
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4472 - loss: 1.2050
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4464 - loss: 1.2057
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4458 - loss: 1.2062
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4456 - loss: 1.2063 - val_accuracy: 0.5018 - val_loss: 1.1587
Epoch 9/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4062 - loss: 1.2908
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4467 - loss: 1.1996 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4384 - loss: 1.2069
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4363 - loss: 1.2092
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4361 - loss: 1.2096
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4366 - loss: 1.2091
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4375 - loss: 1.2084
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4384 - loss: 1.2074
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4390 - loss: 1.2069
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4393 - loss: 1.2066 - val_accuracy: 0.5246 - val_loss: 1.1549
Epoch 10/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4654 - loss: 1.2146 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4577 - loss: 1.2132
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4554 - loss: 1.2085
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4560 - loss: 1.2034
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4553 - loss: 1.2020
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4547 - loss: 1.2013
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4542 - loss: 1.2006
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4539 - loss: 1.2004
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4538 - loss: 1.2002 - val_accuracy: 0.5267 - val_loss: 1.1492
Epoch 11/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5625 - loss: 1.1266
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4919 - loss: 1.1685 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4798 - loss: 1.1747
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4759 - loss: 1.1743
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4734 - loss: 1.1740
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[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4709 - loss: 1.1743
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4698 - loss: 1.1746
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4688 - loss: 1.1751
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4683 - loss: 1.1753 - val_accuracy: 0.5116 - val_loss: 1.1540
Epoch 12/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4600 - loss: 1.1617 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4659 - loss: 1.1685
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4660 - loss: 1.1722
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4653 - loss: 1.1734
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4649 - loss: 1.1737
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4641 - loss: 1.1745
[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4636 - loss: 1.1749
[1m319/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4638 - loss: 1.1749
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4638 - loss: 1.1749 - val_accuracy: 0.5063 - val_loss: 1.1572
Epoch 13/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.1877
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4602 - loss: 1.1798 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4545 - loss: 1.1846
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4566 - loss: 1.1819
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4598 - loss: 1.1784
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4620 - loss: 1.1752
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4636 - loss: 1.1731
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4644 - loss: 1.1718
[1m319/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4651 - loss: 1.1707
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4652 - loss: 1.1704 - val_accuracy: 0.5225 - val_loss: 1.1582
Epoch 14/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4062 - loss: 1.1764
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4969 - loss: 1.1700 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4917 - loss: 1.1614
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4881 - loss: 1.1569
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4857 - loss: 1.1551
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4832 - loss: 1.1552
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4815 - loss: 1.1551
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4803 - loss: 1.1555
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4794 - loss: 1.1559
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4788 - loss: 1.1563 - val_accuracy: 0.5137 - val_loss: 1.1405
Epoch 15/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4625 - loss: 1.1866 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4667 - loss: 1.1761
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4683 - loss: 1.1681
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4674 - loss: 1.1665
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[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4680 - loss: 1.1628
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4680 - loss: 1.1622
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4681 - loss: 1.1621 - val_accuracy: 0.5246 - val_loss: 1.1503
Epoch 16/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4477 - loss: 1.1731 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4625 - loss: 1.1629
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4690 - loss: 1.1571
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4741 - loss: 1.1540
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[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4781 - loss: 1.1522
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4791 - loss: 1.1515
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4796 - loss: 1.1513 - val_accuracy: 0.5390 - val_loss: 1.1545
Epoch 17/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4686 - loss: 1.1627 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4742 - loss: 1.1606
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4768 - loss: 1.1587
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4775 - loss: 1.1579
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[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4772 - loss: 1.1571
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4771 - loss: 1.1566
[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4774 - loss: 1.1559
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4775 - loss: 1.1557 - val_accuracy: 0.5428 - val_loss: 1.1274
Epoch 18/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4903 - loss: 1.1459 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4822 - loss: 1.1481
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4806 - loss: 1.1466
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4800 - loss: 1.1462
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4799 - loss: 1.1457
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4800 - loss: 1.1453
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4803 - loss: 1.1449
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4806 - loss: 1.1448
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4807 - loss: 1.1447 - val_accuracy: 0.5267 - val_loss: 1.1293
Epoch 19/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4526 - loss: 1.1471 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4618 - loss: 1.1461
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4673 - loss: 1.1465
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4710 - loss: 1.1467
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4744 - loss: 1.1457
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4766 - loss: 1.1443
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4780 - loss: 1.1429
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4787 - loss: 1.1420
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4789 - loss: 1.1417 - val_accuracy: 0.5421 - val_loss: 1.1341
Epoch 20/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4781 - loss: 1.1613 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4851 - loss: 1.1406
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4868 - loss: 1.1350
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4877 - loss: 1.1318
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4893 - loss: 1.1283
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4901 - loss: 1.1273
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4903 - loss: 1.1276
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4903 - loss: 1.1277
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4902 - loss: 1.1278 - val_accuracy: 0.5600 - val_loss: 1.1163
Epoch 21/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.2090
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5024 - loss: 1.1224 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4962 - loss: 1.1206
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4940 - loss: 1.1194
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4930 - loss: 1.1178
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4926 - loss: 1.1169
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4924 - loss: 1.1167
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4924 - loss: 1.1168
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4924 - loss: 1.1170
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4924 - loss: 1.1172 - val_accuracy: 0.5530 - val_loss: 1.1098
Epoch 22/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4928 - loss: 1.1040 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5004 - loss: 1.1049
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5004 - loss: 1.1080
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5001 - loss: 1.1097
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5000 - loss: 1.1106
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4998 - loss: 1.1114
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4996 - loss: 1.1124
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4991 - loss: 1.1134
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4990 - loss: 1.1137 - val_accuracy: 0.5544 - val_loss: 1.1155
Epoch 23/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 19ms/step - accuracy: 0.5625 - loss: 0.9861
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4874 - loss: 1.1146 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4921 - loss: 1.1184
[1m123/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4954 - loss: 1.1173
[1m161/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4954 - loss: 1.1181
[1m202/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4955 - loss: 1.1182
[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4962 - loss: 1.1179
[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4968 - loss: 1.1183
[1m321/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4975 - loss: 1.1186
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4976 - loss: 1.1186 - val_accuracy: 0.5678 - val_loss: 1.1645
Epoch 24/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4062 - loss: 1.2057
[1m 42/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4911 - loss: 1.1240 
[1m 83/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4930 - loss: 1.1273
[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4918 - loss: 1.1280
[1m164/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4922 - loss: 1.1255
[1m205/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4942 - loss: 1.1223
[1m243/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4957 - loss: 1.1200
[1m282/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4967 - loss: 1.1186
[1m321/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4972 - loss: 1.1178
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4973 - loss: 1.1177 - val_accuracy: 0.5558 - val_loss: 1.1060
Epoch 25/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4961 - loss: 1.1178 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4981 - loss: 1.1175
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5000 - loss: 1.1167
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5015 - loss: 1.1152
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[1m226/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5044 - loss: 1.1119
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5054 - loss: 1.1105
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5059 - loss: 1.1097
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5061 - loss: 1.1094 - val_accuracy: 0.5621 - val_loss: 1.1209
Epoch 26/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4955 - loss: 1.0896 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4946 - loss: 1.1007
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[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4993 - loss: 1.1071
[1m203/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5010 - loss: 1.1067
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5017 - loss: 1.1064
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5021 - loss: 1.1065
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Epoch 27/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5143 - loss: 1.1107 
[1m 83/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5141 - loss: 1.1135
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5146 - loss: 1.1105
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5136 - loss: 1.1097
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[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5106 - loss: 1.1083
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5103 - loss: 1.1077
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5101 - loss: 1.1075 - val_accuracy: 0.5713 - val_loss: 1.1065
Epoch 28/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5625 - loss: 1.1090
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5358 - loss: 1.0888 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5322 - loss: 1.0771
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5296 - loss: 1.0753
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5283 - loss: 1.0747
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5271 - loss: 1.0754
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5256 - loss: 1.0772
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5240 - loss: 1.0790
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5228 - loss: 1.0804
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5222 - loss: 1.0810 - val_accuracy: 0.5400 - val_loss: 1.1299
Epoch 29/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4882 - loss: 1.1216 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4949 - loss: 1.1165
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[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5082 - loss: 1.1051
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Saved model to disk.

Accuracy capturado en la ejecución 22: 46.86 [%]
F1-score capturado en la ejecución 22: 45.86 [%]

=== EJECUCIÓN 23 ===

--- TRAIN (ejecución 23) ---

--- TEST (ejecución 23) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:34[0m 1s/step
[1m 61/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 843us/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m60/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 865us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 968us/step
Global accuracy score (validation) = 55.23 [%]
Global F1 score (validation) = 54.79 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.11750218 0.14842422 0.72326374 0.01080986]
 [0.04383365 0.07265946 0.87970084 0.00380608]
 [0.21429577 0.21488842 0.5154815  0.05533442]
 ...
 [0.20397648 0.17391603 0.5217952  0.10031231]
 [0.23144528 0.14257629 0.4805923  0.1453861 ]
 [0.22922513 0.18121728 0.5470239  0.04253376]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 55.79 [%]
Global accuracy score (test) = 50.29 [%]
Global F1 score (train) = 56.16 [%]
Global F1 score (test) = 50.52 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.36      0.30      0.33       400
MODERATE-INTENSITY       0.43      0.62      0.51       400
         SEDENTARY       0.57      0.60      0.59       400
VIGOROUS-INTENSITY       0.76      0.49      0.60       345

          accuracy                           0.50      1545
         macro avg       0.53      0.50      0.51      1545
      weighted avg       0.52      0.50      0.50      1545

2025-11-05 10:53:31.950226: 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`.
2025-11-05 10:53:31.961758: 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 00:00:1762336411.975292 3076770 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762336411.979525 3076770 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762336411.989612 3076770 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336411.989635 3076770 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336411.989637 3076770 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336411.989639 3076770 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:53:31.992789: 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 00:00:1762336414.264282 3076770 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 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..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/53
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762336415.864235 3076903 service.cc:152] XLA service 0x7461d40022d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762336415.864269 3076903 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:53:35.903464: 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 00:00:1762336416.074216 3076903 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762336418.256275 3076903 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18:21[0m 3s/step - accuracy: 0.2812 - loss: 2.7765
[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2552 - loss: 2.6719  
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2752 - loss: 2.5085
[1m108/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2797 - loss: 2.3978
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2824 - loss: 2.2985
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2853 - loss: 2.2158
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2881 - loss: 2.1461
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2902 - loss: 2.0897
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2922 - loss: 2.0406
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Epoch 2/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3150 - loss: 1.3987 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3232 - loss: 1.3902
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[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3345 - loss: 1.3791
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[1m224/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3372 - loss: 1.3744
[1m262/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3379 - loss: 1.3728
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3382 - loss: 1.3715
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3383 - loss: 1.3708 - val_accuracy: 0.4772 - val_loss: 1.2433
Epoch 3/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3939 - loss: 1.3151 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3843 - loss: 1.3180
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3818 - loss: 1.3192
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3794 - loss: 1.3198
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3778 - loss: 1.3200
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3771 - loss: 1.3198
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3772 - loss: 1.3190
[1m317/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3772 - loss: 1.3183
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3772 - loss: 1.3181 - val_accuracy: 0.4810 - val_loss: 1.2100
Epoch 4/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1820
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3945 - loss: 1.2881 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3910 - loss: 1.2909
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3881 - loss: 1.2924
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3875 - loss: 1.2937
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3880 - loss: 1.2937
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3888 - loss: 1.2934
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3901 - loss: 1.2925
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3910 - loss: 1.2916
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3915 - loss: 1.2910 - val_accuracy: 0.4803 - val_loss: 1.1894
Epoch 5/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 1.1185
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4190 - loss: 1.2498 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4136 - loss: 1.2578
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4116 - loss: 1.2596
[1m146/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4106 - loss: 1.2614
[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4103 - loss: 1.2611
[1m224/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4097 - loss: 1.2609
[1m263/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4094 - loss: 1.2608
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4097 - loss: 1.2602
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4102 - loss: 1.2597 - val_accuracy: 0.5084 - val_loss: 1.1786
Epoch 6/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4103 - loss: 1.2795 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4144 - loss: 1.2643
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4168 - loss: 1.2561
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4193 - loss: 1.2514
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4216 - loss: 1.2486
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4223 - loss: 1.2477
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4227 - loss: 1.2467
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4234 - loss: 1.2456
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4237 - loss: 1.2449 - val_accuracy: 0.5084 - val_loss: 1.1694
Epoch 7/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2812 - loss: 1.3521
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4020 - loss: 1.2812 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4092 - loss: 1.2653
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4168 - loss: 1.2536
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4206 - loss: 1.2468
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4230 - loss: 1.2419
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4249 - loss: 1.2381
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4263 - loss: 1.2357
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4273 - loss: 1.2340
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4278 - loss: 1.2332 - val_accuracy: 0.5225 - val_loss: 1.1589
Epoch 8/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5312 - loss: 1.0726
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4533 - loss: 1.2123 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4499 - loss: 1.2152
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4475 - loss: 1.2169
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4462 - loss: 1.2176
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4453 - loss: 1.2173
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4442 - loss: 1.2169
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4433 - loss: 1.2168
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4427 - loss: 1.2165
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4427 - loss: 1.2162 - val_accuracy: 0.5190 - val_loss: 1.1548
Epoch 9/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1091
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4744 - loss: 1.1907 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4772 - loss: 1.1932
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4736 - loss: 1.1954
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4701 - loss: 1.1969
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4686 - loss: 1.1964
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4669 - loss: 1.1961
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4655 - loss: 1.1960
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4645 - loss: 1.1957
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4639 - loss: 1.1957 - val_accuracy: 0.5130 - val_loss: 1.1430
Epoch 10/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.0254
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4476 - loss: 1.1754 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4440 - loss: 1.1815
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4426 - loss: 1.1863
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4423 - loss: 1.1879
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4425 - loss: 1.1887
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4428 - loss: 1.1889
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4427 - loss: 1.1891
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4430 - loss: 1.1892
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4433 - loss: 1.1891 - val_accuracy: 0.5130 - val_loss: 1.1474
Epoch 11/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4757 - loss: 1.1503 
[1m 83/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4675 - loss: 1.1636
[1m123/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4658 - loss: 1.1669
[1m163/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4658 - loss: 1.1688
[1m202/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4658 - loss: 1.1699
[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4663 - loss: 1.1706
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4666 - loss: 1.1710
[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4668 - loss: 1.1718
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4667 - loss: 1.1721 - val_accuracy: 0.5242 - val_loss: 1.1566
Epoch 12/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.2778
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4754 - loss: 1.1774 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4764 - loss: 1.1708
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4736 - loss: 1.1723
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4720 - loss: 1.1720
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4705 - loss: 1.1723
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4698 - loss: 1.1729
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4696 - loss: 1.1730
[1m317/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4695 - loss: 1.1731
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4695 - loss: 1.1730 - val_accuracy: 0.5267 - val_loss: 1.1291
Epoch 13/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4658 - loss: 1.1563 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4659 - loss: 1.1574
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4663 - loss: 1.1581
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4659 - loss: 1.1606
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4651 - loss: 1.1625
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4651 - loss: 1.1632
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4652 - loss: 1.1640
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4654 - loss: 1.1647
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4655 - loss: 1.1650 - val_accuracy: 0.5323 - val_loss: 1.1266
Epoch 14/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1776
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4526 - loss: 1.1687 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4601 - loss: 1.1641
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4626 - loss: 1.1634
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4643 - loss: 1.1622
[1m203/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4655 - loss: 1.1619
[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4663 - loss: 1.1617
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4668 - loss: 1.1615
[1m317/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4674 - loss: 1.1614
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4675 - loss: 1.1614 - val_accuracy: 0.5355 - val_loss: 1.1574
Epoch 15/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.0877
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4741 - loss: 1.1531 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4684 - loss: 1.1595
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4675 - loss: 1.1607
[1m161/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4694 - loss: 1.1600
[1m200/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4705 - loss: 1.1598
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4711 - loss: 1.1598
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4716 - loss: 1.1593
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4723 - loss: 1.1589
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4725 - loss: 1.1588 - val_accuracy: 0.5425 - val_loss: 1.1299
Epoch 16/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4646 - loss: 1.1591 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4718 - loss: 1.1571
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4761 - loss: 1.1536
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4766 - loss: 1.1539
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4764 - loss: 1.1552
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4761 - loss: 1.1561
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4760 - loss: 1.1567
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4761 - loss: 1.1570
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4761 - loss: 1.1570 - val_accuracy: 0.5456 - val_loss: 1.1290
Epoch 17/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.5312 - loss: 1.0325
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4842 - loss: 1.1568 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4810 - loss: 1.1558
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4782 - loss: 1.1571
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4769 - loss: 1.1573
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4765 - loss: 1.1568
[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4765 - loss: 1.1560
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4767 - loss: 1.1549
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4769 - loss: 1.1539
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4770 - loss: 1.1534 - val_accuracy: 0.5390 - val_loss: 1.1281
Epoch 18/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.2031
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4758 - loss: 1.1626 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4835 - loss: 1.1527
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4851 - loss: 1.1506
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4852 - loss: 1.1486
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4849 - loss: 1.1478
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4850 - loss: 1.1471
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4848 - loss: 1.1467
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4847 - loss: 1.1464
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4847 - loss: 1.1463 - val_accuracy: 0.5386 - val_loss: 1.1101
Epoch 19/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.4062 - loss: 1.1169
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4934 - loss: 1.1275 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4956 - loss: 1.1342
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4967 - loss: 1.1337
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4969 - loss: 1.1330
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4963 - loss: 1.1334
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4950 - loss: 1.1341
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4940 - loss: 1.1344
[1m319/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4932 - loss: 1.1348
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4931 - loss: 1.1349 - val_accuracy: 0.5460 - val_loss: 1.1119
Epoch 20/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.2987
[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5001 - loss: 1.1253 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4996 - loss: 1.1262
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5005 - loss: 1.1239
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5004 - loss: 1.1235
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5001 - loss: 1.1237
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4992 - loss: 1.1247
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4982 - loss: 1.1259
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4971 - loss: 1.1267
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4965 - loss: 1.1271 - val_accuracy: 0.5400 - val_loss: 1.1012
Epoch 21/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5136 - loss: 1.0980 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5129 - loss: 1.1035
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5085 - loss: 1.1084
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5053 - loss: 1.1122
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5029 - loss: 1.1146
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5015 - loss: 1.1158
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5005 - loss: 1.1171
[1m319/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4999 - loss: 1.1181
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4997 - loss: 1.1183 - val_accuracy: 0.5562 - val_loss: 1.1060
Epoch 22/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.3750 - loss: 1.1792
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4948 - loss: 1.1317 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4983 - loss: 1.1266
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5001 - loss: 1.1237
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4992 - loss: 1.1237
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4979 - loss: 1.1247
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4974 - loss: 1.1249
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4970 - loss: 1.1246
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4967 - loss: 1.1246
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4965 - loss: 1.1246 - val_accuracy: 0.5456 - val_loss: 1.1218
Epoch 23/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4790 - loss: 1.1364 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4810 - loss: 1.1332
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4817 - loss: 1.1318
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4835 - loss: 1.1284
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4854 - loss: 1.1259
[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4866 - loss: 1.1247
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4877 - loss: 1.1237
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4886 - loss: 1.1234
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4889 - loss: 1.1234 - val_accuracy: 0.5499 - val_loss: 1.1099
Epoch 24/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.3125 - loss: 1.4675
[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4828 - loss: 1.1374 
[1m 68/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4855 - loss: 1.1294
[1m105/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4864 - loss: 1.1286
[1m145/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4877 - loss: 1.1297
[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4892 - loss: 1.1293
[1m217/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4906 - loss: 1.1283
[1m255/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4918 - loss: 1.1272
[1m294/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4928 - loss: 1.1265
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4932 - loss: 1.1263 - val_accuracy: 0.5583 - val_loss: 1.0897
Epoch 25/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 1.2553
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4782 - loss: 1.1505 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4899 - loss: 1.1344
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4951 - loss: 1.1257
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4973 - loss: 1.1223
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4989 - loss: 1.1193
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4998 - loss: 1.1174
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5007 - loss: 1.1159
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5013 - loss: 1.1150
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5017 - loss: 1.1146 - val_accuracy: 0.5730 - val_loss: 1.1001
Epoch 26/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4889 - loss: 1.1300 
[1m 70/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4879 - loss: 1.1334
[1m105/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4883 - loss: 1.1323
[1m144/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4911 - loss: 1.1278
[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4943 - loss: 1.1228
[1m220/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4972 - loss: 1.1185
[1m256/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4992 - loss: 1.1158
[1m294/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5003 - loss: 1.1145
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5013 - loss: 1.1137 - val_accuracy: 0.5699 - val_loss: 1.0808
Epoch 27/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 0.9266
[1m 43/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5178 - loss: 1.0982 
[1m 84/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5174 - loss: 1.1031
[1m127/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5156 - loss: 1.1063
[1m166/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5129 - loss: 1.1074
[1m203/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5113 - loss: 1.1077
[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5105 - loss: 1.1074
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5097 - loss: 1.1074
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5091 - loss: 1.1074
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5089 - loss: 1.1073 - val_accuracy: 0.5685 - val_loss: 1.0986
Epoch 28/53

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[1m 42/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5205 - loss: 1.1089 
[1m 82/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5194 - loss: 1.1100
[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5174 - loss: 1.1121
[1m163/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5159 - loss: 1.1131
[1m199/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5144 - loss: 1.1133
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5138 - loss: 1.1121
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5128 - loss: 1.1116
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5121 - loss: 1.1114
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5118 - loss: 1.1112 - val_accuracy: 0.5699 - val_loss: 1.0907
Epoch 29/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.6562 - loss: 0.9993
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5456 - loss: 1.0727 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5361 - loss: 1.0805
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5303 - loss: 1.0842
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5266 - loss: 1.0863
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5244 - loss: 1.0879
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5232 - loss: 1.0891
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5221 - loss: 1.0898
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5214 - loss: 1.0902
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5210 - loss: 1.0907 - val_accuracy: 0.5657 - val_loss: 1.0867
Epoch 30/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5938 - loss: 0.9891
[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5206 - loss: 1.0734 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5197 - loss: 1.0761
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5200 - loss: 1.0781
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5190 - loss: 1.0815
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5179 - loss: 1.0847
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5170 - loss: 1.0872
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5165 - loss: 1.0890
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5157 - loss: 1.0906
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5154 - loss: 1.0912 - val_accuracy: 0.5769 - val_loss: 1.0820
Epoch 31/53

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 399ms/step
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Saved model to disk.

Accuracy capturado en la ejecución 23: 50.29 [%]
F1-score capturado en la ejecución 23: 50.52 [%]

=== EJECUCIÓN 24 ===

--- TRAIN (ejecución 24) ---

--- TEST (ejecución 24) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:23[0m 988ms/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m64/89[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 795us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 942us/step
Global accuracy score (validation) = 57.16 [%]
Global F1 score (validation) = 55.15 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.10703216 0.12720114 0.7542579  0.01150881]
 [0.29154798 0.40057042 0.13421346 0.17366812]
 [0.11021535 0.12659554 0.75179213 0.01139694]
 ...
 [0.21623984 0.13145107 0.58109957 0.07120954]
 [0.23591812 0.12559529 0.5194255  0.11906105]
 [0.17274176 0.13571894 0.66045403 0.03108534]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 55.48 [%]
Global accuracy score (test) = 52.3 [%]
Global F1 score (train) = 54.55 [%]
Global F1 score (test) = 51.04 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.37      0.21      0.27       400
MODERATE-INTENSITY       0.48      0.69      0.56       400
         SEDENTARY       0.54      0.70      0.61       400
VIGOROUS-INTENSITY       0.78      0.49      0.60       345

          accuracy                           0.52      1545
         macro avg       0.54      0.52      0.51      1545
      weighted avg       0.53      0.52      0.51      1545

2025-11-05 10:54:10.153247: 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`.
2025-11-05 10:54:10.164722: 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 00:00:1762336450.178330 3080632 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762336450.182554 3080632 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762336450.192361 3080632 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336450.192381 3080632 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336450.192383 3080632 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336450.192385 3080632 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:54:10.195677: 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 00:00:1762336452.453572 3080632 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 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..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/53
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762336454.067809 3080763 service.cc:152] XLA service 0x7c189000c600 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762336454.067881 3080763 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:54:14.101514: 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 00:00:1762336454.271597 3080763 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762336456.439273 3080763 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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[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2756 - loss: 2.2054
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[1m258/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2799 - loss: 2.0908
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2821 - loss: 2.0414
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Epoch 2/53

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[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3382 - loss: 1.4080
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3413 - loss: 1.3979
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3429 - loss: 1.3916
[1m199/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3440 - loss: 1.3872
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3449 - loss: 1.3841
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3458 - loss: 1.3813
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3467 - loss: 1.3787
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Epoch 3/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3924 - loss: 1.3071 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3866 - loss: 1.3144
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3838 - loss: 1.3162
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3829 - loss: 1.3166
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[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3807 - loss: 1.3173
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3801 - loss: 1.3171
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3796 - loss: 1.3168
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3796 - loss: 1.3166 - val_accuracy: 0.5032 - val_loss: 1.2121
Epoch 4/53

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[1m 42/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4293 - loss: 1.2573 
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[1m127/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4108 - loss: 1.2733
[1m166/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4095 - loss: 1.2740
[1m207/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4080 - loss: 1.2749
[1m244/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4067 - loss: 1.2755
[1m286/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4054 - loss: 1.2761
[1m326/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4045 - loss: 1.2765
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Epoch 5/53

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[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4095 - loss: 1.2661
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4066 - loss: 1.2662
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4066 - loss: 1.2663
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[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4093 - loss: 1.2657
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4107 - loss: 1.2647
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Epoch 6/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4213 - loss: 1.2635 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4268 - loss: 1.2578
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4286 - loss: 1.2567
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4303 - loss: 1.2552
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[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4301 - loss: 1.2526
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4298 - loss: 1.2515
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4296 - loss: 1.2504
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4296 - loss: 1.2501 - val_accuracy: 0.5032 - val_loss: 1.1588
Epoch 7/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4929 - loss: 1.1765 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4776 - loss: 1.1928
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4692 - loss: 1.2002
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4630 - loss: 1.2054
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4595 - loss: 1.2088
[1m224/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4571 - loss: 1.2109
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4549 - loss: 1.2127
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4533 - loss: 1.2142
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4524 - loss: 1.2147 - val_accuracy: 0.5018 - val_loss: 1.1636
Epoch 8/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3870 - loss: 1.2370 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4065 - loss: 1.2234
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4168 - loss: 1.2200
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4230 - loss: 1.2180
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4263 - loss: 1.2171
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4292 - loss: 1.2162
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4314 - loss: 1.2155
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4332 - loss: 1.2150
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4342 - loss: 1.2146 - val_accuracy: 0.5228 - val_loss: 1.1561
Epoch 9/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.3125 - loss: 1.2218
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4216 - loss: 1.2158 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4187 - loss: 1.2159
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4234 - loss: 1.2153
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4292 - loss: 1.2126
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4341 - loss: 1.2103
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4379 - loss: 1.2085
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4406 - loss: 1.2070
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4430 - loss: 1.2057
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4441 - loss: 1.2052 - val_accuracy: 0.5348 - val_loss: 1.1524
Epoch 10/53

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[1m 42/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4402 - loss: 1.1946 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4493 - loss: 1.1933
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4530 - loss: 1.1960
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4554 - loss: 1.1947
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4570 - loss: 1.1927
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4581 - loss: 1.1917
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4590 - loss: 1.1913
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4595 - loss: 1.1910
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4597 - loss: 1.1909 - val_accuracy: 0.5246 - val_loss: 1.1385
Epoch 11/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 19ms/step - accuracy: 0.3750 - loss: 1.2685
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4429 - loss: 1.2228 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4519 - loss: 1.2068
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4566 - loss: 1.2004
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4583 - loss: 1.1974
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4590 - loss: 1.1956
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4594 - loss: 1.1945
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4600 - loss: 1.1935
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4604 - loss: 1.1928
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4608 - loss: 1.1924 - val_accuracy: 0.5320 - val_loss: 1.1363
Epoch 12/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.3750 - loss: 1.2611
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4342 - loss: 1.2218 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4431 - loss: 1.2039
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4512 - loss: 1.1936
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4561 - loss: 1.1867
[1m199/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4590 - loss: 1.1826
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4611 - loss: 1.1797
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4628 - loss: 1.1772
[1m320/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4634 - loss: 1.1762
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4635 - loss: 1.1761 - val_accuracy: 0.5179 - val_loss: 1.1480
Epoch 13/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3125 - loss: 1.2867
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4311 - loss: 1.1851 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4441 - loss: 1.1846
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4478 - loss: 1.1825
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4513 - loss: 1.1801
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4540 - loss: 1.1780
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4562 - loss: 1.1768
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4582 - loss: 1.1760
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4595 - loss: 1.1755
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4601 - loss: 1.1752 - val_accuracy: 0.5411 - val_loss: 1.1300
Epoch 14/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1044
[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4670 - loss: 1.1461 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4723 - loss: 1.1488
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4768 - loss: 1.1487
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4790 - loss: 1.1488
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4795 - loss: 1.1491
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4804 - loss: 1.1487
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4809 - loss: 1.1485
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4810 - loss: 1.1490
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4810 - loss: 1.1493 - val_accuracy: 0.5277 - val_loss: 1.1256
Epoch 15/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4722 - loss: 1.1459 
[1m 82/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4775 - loss: 1.1487
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4770 - loss: 1.1508
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4757 - loss: 1.1536
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4746 - loss: 1.1560
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4740 - loss: 1.1574
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4738 - loss: 1.1580
[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4737 - loss: 1.1586
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4737 - loss: 1.1586 - val_accuracy: 0.5523 - val_loss: 1.1465
Epoch 16/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5072 - loss: 1.1056 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4931 - loss: 1.1198
[1m109/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4878 - loss: 1.1257
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4855 - loss: 1.1304
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4841 - loss: 1.1353
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4832 - loss: 1.1388
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4828 - loss: 1.1405
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4826 - loss: 1.1419
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4826 - loss: 1.1424 - val_accuracy: 0.5270 - val_loss: 1.1439
Epoch 17/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4567 - loss: 1.1485 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4619 - loss: 1.1452
[1m108/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4656 - loss: 1.1476
[1m144/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4675 - loss: 1.1481
[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4693 - loss: 1.1473
[1m219/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4709 - loss: 1.1466
[1m256/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4723 - loss: 1.1460
[1m296/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4738 - loss: 1.1453
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4746 - loss: 1.1450 - val_accuracy: 0.5316 - val_loss: 1.1312
Epoch 18/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5312 - loss: 1.1002
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4968 - loss: 1.1461 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4943 - loss: 1.1428
[1m109/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4934 - loss: 1.1415
[1m142/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4918 - loss: 1.1415
[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4905 - loss: 1.1415
[1m220/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4898 - loss: 1.1415
[1m256/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4894 - loss: 1.1413
[1m293/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4889 - loss: 1.1412
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4884 - loss: 1.1413 - val_accuracy: 0.5425 - val_loss: 1.1166
Epoch 19/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.2821
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4912 - loss: 1.1791 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4892 - loss: 1.1737
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4890 - loss: 1.1657
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4891 - loss: 1.1602
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4891 - loss: 1.1578
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4894 - loss: 1.1556
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4895 - loss: 1.1537
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4897 - loss: 1.1521
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4898 - loss: 1.1516 - val_accuracy: 0.5348 - val_loss: 1.1322
Epoch 20/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4818 - loss: 1.1432 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4891 - loss: 1.1319
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4894 - loss: 1.1301
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4911 - loss: 1.1283
[1m201/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4921 - loss: 1.1280
[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4929 - loss: 1.1282
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4933 - loss: 1.1284
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4936 - loss: 1.1283
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4937 - loss: 1.1284 - val_accuracy: 0.5530 - val_loss: 1.1195
Epoch 21/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1741
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4804 - loss: 1.1125 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4856 - loss: 1.1106
[1m109/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4872 - loss: 1.1110
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4879 - loss: 1.1112
[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4882 - loss: 1.1131
[1m217/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4885 - loss: 1.1145
[1m256/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4887 - loss: 1.1160
[1m296/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4887 - loss: 1.1174
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4888 - loss: 1.1186 - val_accuracy: 0.5428 - val_loss: 1.1053
Epoch 22/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.0968
[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5104 - loss: 1.0604 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5186 - loss: 1.0664
[1m107/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5191 - loss: 1.0718
[1m144/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5176 - loss: 1.0778
[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5160 - loss: 1.0823
[1m217/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5145 - loss: 1.0861
[1m251/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5131 - loss: 1.0891
[1m288/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5120 - loss: 1.0918
[1m325/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5108 - loss: 1.0944
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5107 - loss: 1.0946 - val_accuracy: 0.5530 - val_loss: 1.1025
Epoch 23/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1156
[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4869 - loss: 1.1232 
[1m 70/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5048 - loss: 1.1124
[1m108/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5066 - loss: 1.1148
[1m145/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5065 - loss: 1.1183
[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5061 - loss: 1.1193
[1m224/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5053 - loss: 1.1197
[1m262/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5046 - loss: 1.1201
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5042 - loss: 1.1200
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5040 - loss: 1.1198 - val_accuracy: 0.5439 - val_loss: 1.1008
Epoch 24/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.0962
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5042 - loss: 1.1216 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4971 - loss: 1.1276
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4965 - loss: 1.1278
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4980 - loss: 1.1247
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4992 - loss: 1.1221
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5005 - loss: 1.1198
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5013 - loss: 1.1184
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5015 - loss: 1.1177
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5015 - loss: 1.1174 - val_accuracy: 0.5600 - val_loss: 1.0897
Epoch 25/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5625 - loss: 1.1283
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5315 - loss: 1.0887 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5317 - loss: 1.0871
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5282 - loss: 1.0866
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5257 - loss: 1.0875
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5236 - loss: 1.0892
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5223 - loss: 1.0902
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5211 - loss: 1.0911
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5199 - loss: 1.0921
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5193 - loss: 1.0927 - val_accuracy: 0.5636 - val_loss: 1.1111
Epoch 26/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1195
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5071 - loss: 1.1169 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5023 - loss: 1.1214
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5036 - loss: 1.1186
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5046 - loss: 1.1155
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5056 - loss: 1.1127
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5053 - loss: 1.1111
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5047 - loss: 1.1102
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5047 - loss: 1.1091
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5048 - loss: 1.1086 - val_accuracy: 0.5611 - val_loss: 1.1161
Epoch 27/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.0167
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4933 - loss: 1.0795 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4977 - loss: 1.0954
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5026 - loss: 1.0981
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5039 - loss: 1.1002
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5057 - loss: 1.0994
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5076 - loss: 1.0983
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5089 - loss: 1.0979
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5098 - loss: 1.0979
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5101 - loss: 1.0979 - val_accuracy: 0.5653 - val_loss: 1.1114
Epoch 28/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 0.8583
[1m 42/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5411 - loss: 1.0457 
[1m 82/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5337 - loss: 1.0618
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5304 - loss: 1.0676
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5285 - loss: 1.0706
[1m202/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5267 - loss: 1.0732
[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5256 - loss: 1.0742
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5247 - loss: 1.0755
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5237 - loss: 1.0768
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5234 - loss: 1.0773 - val_accuracy: 0.5681 - val_loss: 1.0900
Epoch 29/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6250 - loss: 1.0019
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5439 - loss: 1.0508 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5370 - loss: 1.0608
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5310 - loss: 1.0673
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5283 - loss: 1.0722
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5273 - loss: 1.0747
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5265 - loss: 1.0765
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5256 - loss: 1.0781
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 393ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 16ms/step  
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Saved model to disk.

Accuracy capturado en la ejecución 24: 52.3 [%]
F1-score capturado en la ejecución 24: 51.04 [%]

=== EJECUCIÓN 25 ===

--- TRAIN (ejecución 25) ---

--- TEST (ejecución 25) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:24[0m 994ms/step
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[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 760us/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m62/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 831us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 948us/step
Global accuracy score (validation) = 57.02 [%]
Global F1 score (validation) = 54.78 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.1384987  0.12188396 0.68242145 0.05719583]
 [0.15386775 0.15577951 0.63408995 0.05626282]
 [0.2954329  0.41751918 0.12045551 0.16659239]
 ...
 [0.18731292 0.14989185 0.57789296 0.08490229]
 [0.23310226 0.15644643 0.34018365 0.27026772]
 [0.13637607 0.12320689 0.6934088  0.04700836]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 55.83 [%]
Global accuracy score (test) = 52.88 [%]
Global F1 score (train) = 55.27 [%]
Global F1 score (test) = 52.45 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.41      0.28      0.33       400
MODERATE-INTENSITY       0.47      0.57      0.52       400
         SEDENTARY       0.52      0.72      0.61       400
VIGOROUS-INTENSITY       0.77      0.55      0.64       345

          accuracy                           0.53      1545
         macro avg       0.55      0.53      0.52      1545
      weighted avg       0.54      0.53      0.52      1545

2025-11-05 10:54:47.113764: 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`.
2025-11-05 10:54:47.125138: 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 00:00:1762336487.138423 3084308 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762336487.142489 3084308 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762336487.152134 3084308 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336487.152157 3084308 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336487.152159 3084308 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336487.152160 3084308 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:54:47.155236: 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 00:00:1762336489.361557 3084308 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 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..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/53
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762336490.977612 3084448 service.cc:152] XLA service 0x7c675401d0a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762336490.977653 3084448 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:54:51.011602: 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 00:00:1762336491.175657 3084448 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762336493.363321 3084448 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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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2982 - loss: 2.5143  
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2979 - loss: 2.4090
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2995 - loss: 2.3010
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3018 - loss: 2.2204
[1m199/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3034 - loss: 2.1461
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3047 - loss: 2.0862
[1m282/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3062 - loss: 2.0340
[1m324/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3079 - loss: 1.9892
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 10ms/step - accuracy: 0.3081 - loss: 1.9843 - val_accuracy: 0.4614 - val_loss: 1.2605
Epoch 2/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3750 - loss: 1.3291
[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3429 - loss: 1.3675 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3379 - loss: 1.3708
[1m110/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3377 - loss: 1.3695
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3391 - loss: 1.3665
[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3407 - loss: 1.3639
[1m221/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3426 - loss: 1.3616
[1m260/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3448 - loss: 1.3587
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3466 - loss: 1.3562
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3477 - loss: 1.3545 - val_accuracy: 0.4853 - val_loss: 1.2425
Epoch 3/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3796 - loss: 1.3324 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3872 - loss: 1.3202
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3875 - loss: 1.3171
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3865 - loss: 1.3153
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3864 - loss: 1.3130
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3866 - loss: 1.3111
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3870 - loss: 1.3095
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3875 - loss: 1.3081
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3877 - loss: 1.3076 - val_accuracy: 0.5007 - val_loss: 1.2402
Epoch 4/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.1562 - loss: 1.3275
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3741 - loss: 1.2762 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3823 - loss: 1.2783
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3861 - loss: 1.2796
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3883 - loss: 1.2814
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3903 - loss: 1.2813
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3922 - loss: 1.2805
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3935 - loss: 1.2794
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3950 - loss: 1.2782
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3956 - loss: 1.2779 - val_accuracy: 0.5140 - val_loss: 1.2051
Epoch 5/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.3200
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4259 - loss: 1.2588 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4216 - loss: 1.2549
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4214 - loss: 1.2537
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4213 - loss: 1.2529
[1m201/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4213 - loss: 1.2521
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4218 - loss: 1.2515
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4226 - loss: 1.2510
[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4232 - loss: 1.2507
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4233 - loss: 1.2507 - val_accuracy: 0.5172 - val_loss: 1.1834
Epoch 6/53

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[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4234 - loss: 1.2293
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[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4242 - loss: 1.2292
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Epoch 7/53

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[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4449 - loss: 1.2347
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[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4410 - loss: 1.2254
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[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4372 - loss: 1.2227
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Epoch 8/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4556 - loss: 1.2000 
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[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4465 - loss: 1.2115
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[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4456 - loss: 1.2113
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4451 - loss: 1.2111
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4447 - loss: 1.2111
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4445 - loss: 1.2111 - val_accuracy: 0.5130 - val_loss: 1.1525
Epoch 9/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5625 - loss: 1.1459
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4920 - loss: 1.1720 
[1m 70/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4814 - loss: 1.1734
[1m108/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4743 - loss: 1.1766
[1m145/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4686 - loss: 1.1807
[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4647 - loss: 1.1841
[1m224/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4619 - loss: 1.1860
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4602 - loss: 1.1874
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4587 - loss: 1.1888
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4580 - loss: 1.1895 - val_accuracy: 0.5105 - val_loss: 1.1457
Epoch 10/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4573 - loss: 1.2007 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4635 - loss: 1.1910
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4657 - loss: 1.1832 - val_accuracy: 0.5032 - val_loss: 1.1493
Epoch 11/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4556 - loss: 1.1703 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4613 - loss: 1.1655
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4605 - loss: 1.1668
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4605 - loss: 1.1673
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4596 - loss: 1.1679
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4594 - loss: 1.1677
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4594 - loss: 1.1677
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4599 - loss: 1.1678
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4600 - loss: 1.1680 - val_accuracy: 0.5130 - val_loss: 1.1436
Epoch 12/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1269
[1m 43/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4752 - loss: 1.1735 
[1m 83/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4773 - loss: 1.1670
[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4755 - loss: 1.1688
[1m164/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4741 - loss: 1.1707
[1m205/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4729 - loss: 1.1718
[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4724 - loss: 1.1722
[1m284/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4722 - loss: 1.1726
[1m324/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4723 - loss: 1.1726
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4723 - loss: 1.1725 - val_accuracy: 0.5144 - val_loss: 1.1397
Epoch 13/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5625 - loss: 1.0409
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4642 - loss: 1.1426 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4606 - loss: 1.1530
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4609 - loss: 1.1572
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4614 - loss: 1.1595
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4620 - loss: 1.1607
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4631 - loss: 1.1614
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4642 - loss: 1.1621
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4652 - loss: 1.1628
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4656 - loss: 1.1630 - val_accuracy: 0.5190 - val_loss: 1.1418
Epoch 14/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4375 - loss: 1.2170
[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4868 - loss: 1.1568 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4851 - loss: 1.1634
[1m107/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4847 - loss: 1.1628
[1m143/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4826 - loss: 1.1645
[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4808 - loss: 1.1664
[1m222/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4791 - loss: 1.1678
[1m259/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4779 - loss: 1.1681
[1m297/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4774 - loss: 1.1680
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4773 - loss: 1.1674 - val_accuracy: 0.5365 - val_loss: 1.1345
Epoch 15/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1486
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5020 - loss: 1.1304 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4899 - loss: 1.1428
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4868 - loss: 1.1458
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4843 - loss: 1.1484
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4820 - loss: 1.1512
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[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4803 - loss: 1.1533
[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4802 - loss: 1.1536
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4802 - loss: 1.1536 - val_accuracy: 0.5235 - val_loss: 1.1286
Epoch 16/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5061 - loss: 1.1153 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4956 - loss: 1.1277
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4910 - loss: 1.1343
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4880 - loss: 1.1390
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[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4866 - loss: 1.1411
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4863 - loss: 1.1418
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4859 - loss: 1.1425
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4858 - loss: 1.1427 - val_accuracy: 0.5298 - val_loss: 1.1228
Epoch 17/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 1.0388
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4857 - loss: 1.1619 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4860 - loss: 1.1613
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4859 - loss: 1.1612
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4854 - loss: 1.1607
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4854 - loss: 1.1595
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4851 - loss: 1.1585
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4851 - loss: 1.1572
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4855 - loss: 1.1559
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4856 - loss: 1.1554 - val_accuracy: 0.5407 - val_loss: 1.1295
Epoch 18/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6875 - loss: 0.9170
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5234 - loss: 1.1141 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5099 - loss: 1.1271
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5046 - loss: 1.1318
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5018 - loss: 1.1346
[1m202/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4998 - loss: 1.1369
[1m243/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4983 - loss: 1.1388
[1m284/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4970 - loss: 1.1401
[1m324/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4960 - loss: 1.1408
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4958 - loss: 1.1408 - val_accuracy: 0.5327 - val_loss: 1.1228
Epoch 19/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5625 - loss: 0.9975
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5041 - loss: 1.1032 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5008 - loss: 1.1144
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4973 - loss: 1.1226
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4954 - loss: 1.1275
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4939 - loss: 1.1299
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4933 - loss: 1.1312
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4928 - loss: 1.1321
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4924 - loss: 1.1324
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4922 - loss: 1.1324 - val_accuracy: 0.5446 - val_loss: 1.1148
Epoch 20/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1112
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4860 - loss: 1.1331 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4901 - loss: 1.1287
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[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4947 - loss: 1.1218
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4951 - loss: 1.1210
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4947 - loss: 1.1212
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4948 - loss: 1.1211
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4944 - loss: 1.1215
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4941 - loss: 1.1218 - val_accuracy: 0.5418 - val_loss: 1.1204
Epoch 21/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6250 - loss: 0.9624
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5150 - loss: 1.1020 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5085 - loss: 1.1105
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5085 - loss: 1.1105
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5083 - loss: 1.1114
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5080 - loss: 1.1124
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5072 - loss: 1.1135
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5059 - loss: 1.1154
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5048 - loss: 1.1171
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5043 - loss: 1.1178 - val_accuracy: 0.5390 - val_loss: 1.1098
Epoch 22/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.0967
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5165 - loss: 1.0928 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5092 - loss: 1.0963
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5049 - loss: 1.1012
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5033 - loss: 1.1042
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5025 - loss: 1.1072
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5021 - loss: 1.1092
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5015 - loss: 1.1108
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5012 - loss: 1.1119
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5009 - loss: 1.1124 - val_accuracy: 0.5593 - val_loss: 1.1123
Epoch 23/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5312 - loss: 1.1114
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4832 - loss: 1.1244 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4837 - loss: 1.1254
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4848 - loss: 1.1256
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4848 - loss: 1.1275
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4854 - loss: 1.1280
[1m224/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4863 - loss: 1.1280
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4872 - loss: 1.1276
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4883 - loss: 1.1271
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4891 - loss: 1.1265 - val_accuracy: 0.5579 - val_loss: 1.1061
Epoch 24/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5625 - loss: 1.1144
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5140 - loss: 1.1101 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5050 - loss: 1.1091
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5025 - loss: 1.1095
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5027 - loss: 1.1086
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5023 - loss: 1.1095
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5019 - loss: 1.1102
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5019 - loss: 1.1104
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5017 - loss: 1.1111
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5017 - loss: 1.1115 - val_accuracy: 0.5551 - val_loss: 1.1186
Epoch 25/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.1854
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5017 - loss: 1.1311 
[1m 82/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5076 - loss: 1.1241
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5094 - loss: 1.1188
[1m161/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5105 - loss: 1.1145
[1m202/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5109 - loss: 1.1122
[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5107 - loss: 1.1112
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5106 - loss: 1.1106
[1m317/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5105 - loss: 1.1101
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5104 - loss: 1.1100 - val_accuracy: 0.5530 - val_loss: 1.1012
Epoch 26/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 0.9196
[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5246 - loss: 1.0970 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5129 - loss: 1.1078
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5110 - loss: 1.1074
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5108 - loss: 1.1071
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5100 - loss: 1.1071
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5092 - loss: 1.1070
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5091 - loss: 1.1065
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5090 - loss: 1.1060
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5089 - loss: 1.1059 - val_accuracy: 0.5600 - val_loss: 1.1176
Epoch 27/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5625 - loss: 1.0953
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5084 - loss: 1.1103 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5069 - loss: 1.1061
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[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5074 - loss: 1.1041
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5084 - loss: 1.1026
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5095 - loss: 1.1015
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5100 - loss: 1.1007
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5101 - loss: 1.1003
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5102 - loss: 1.1002 - val_accuracy: 0.5741 - val_loss: 1.1150
Epoch 28/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0739
[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5109 - loss: 1.0974 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5115 - loss: 1.0891
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5123 - loss: 1.0861
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5132 - loss: 1.0851
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5133 - loss: 1.0860
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5130 - loss: 1.0872
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5125 - loss: 1.0884
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5123 - loss: 1.0892
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5123 - loss: 1.0893 - val_accuracy: 0.5671 - val_loss: 1.0900
Epoch 29/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 1.0382
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4845 - loss: 1.0950 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4861 - loss: 1.1016
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4925 - loss: 1.0983
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4974 - loss: 1.0968
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5001 - loss: 1.0967
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5019 - loss: 1.0962
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5033 - loss: 1.0959
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5044 - loss: 1.0958
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5049 - loss: 1.0957 - val_accuracy: 0.5755 - val_loss: 1.1062
Epoch 30/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5938 - loss: 0.9379
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5253 - loss: 1.0976 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5237 - loss: 1.0914
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5215 - loss: 1.0907
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5199 - loss: 1.0909
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5193 - loss: 1.0911
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5191 - loss: 1.0909
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5191 - loss: 1.0906
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5193 - loss: 1.0899
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5195 - loss: 1.0895 - val_accuracy: 0.5600 - val_loss: 1.1004
Epoch 31/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 26ms/step - accuracy: 0.4375 - loss: 1.2437
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4990 - loss: 1.0885 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5109 - loss: 1.0826
[1m108/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5161 - loss: 1.0809
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5186 - loss: 1.0806
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5198 - loss: 1.0801
[1m224/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5203 - loss: 1.0799
[1m261/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5204 - loss: 1.0798
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5200 - loss: 1.0800
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5199 - loss: 1.0798 - val_accuracy: 0.5783 - val_loss: 1.1109
Epoch 32/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.2437
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5415 - loss: 1.0377 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5385 - loss: 1.0459
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5374 - loss: 1.0508
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5358 - loss: 1.0558
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5336 - loss: 1.0594
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5316 - loss: 1.0618
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5298 - loss: 1.0636
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5285 - loss: 1.0653
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5280 - loss: 1.0659 - val_accuracy: 0.5758 - val_loss: 1.0976
Epoch 33/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.7188 - loss: 1.0325
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5475 - loss: 1.0583 
[1m 82/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5334 - loss: 1.0623
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[1m164/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5286 - loss: 1.0658
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[1m282/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5250 - loss: 1.0695
[1m321/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5246 - loss: 1.0701
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5246 - loss: 1.0703 - val_accuracy: 0.5657 - val_loss: 1.1053

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 401ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 17ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 17ms/step
Saved model to disk.

Accuracy capturado en la ejecución 25: 52.88 [%]
F1-score capturado en la ejecución 25: 52.45 [%]

=== EJECUCIÓN 26 ===

--- TRAIN (ejecución 26) ---

--- TEST (ejecución 26) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:35[0m 1s/step
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 15ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m70/89[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 731us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 865us/step
Global accuracy score (validation) = 57.41 [%]
Global F1 score (validation) = 56.11 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.28209126 0.30253065 0.21439561 0.20098253]
 [0.23217241 0.20485663 0.51031226 0.05265861]
 [0.29885942 0.38515514 0.13214387 0.18384159]
 ...
 [0.188784   0.1910532  0.5522377  0.06792513]
 [0.24179405 0.18095046 0.42957914 0.14767635]
 [0.18962264 0.1311878  0.6257305  0.05345912]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 57.12 [%]
Global accuracy score (test) = 52.62 [%]
Global F1 score (train) = 56.92 [%]
Global F1 score (test) = 51.78 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.43      0.29      0.34       400
MODERATE-INTENSITY       0.47      0.60      0.53       400
         SEDENTARY       0.54      0.75      0.63       400
VIGOROUS-INTENSITY       0.79      0.46      0.58       345

          accuracy                           0.53      1545
         macro avg       0.55      0.52      0.52      1545
      weighted avg       0.55      0.53      0.52      1545

2025-11-05 10:55:26.463552: 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`.
2025-11-05 10:55:26.475041: 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 00:00:1762336526.488347 3088362 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762336526.492532 3088362 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762336526.502407 3088362 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336526.502424 3088362 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336526.502426 3088362 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336526.502427 3088362 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:55:26.505722: 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 00:00:1762336528.752988 3088362 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 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..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/53
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762336530.374852 3088488 service.cc:152] XLA service 0x77a084016910 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762336530.374901 3088488 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:55:30.413045: 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 00:00:1762336530.578906 3088488 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762336532.729092 3088488 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18:17[0m 3s/step - accuracy: 0.1250 - loss: 3.3352
[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2281 - loss: 2.7390  
[1m 67/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2527 - loss: 2.5724
[1m105/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2637 - loss: 2.4548
[1m145/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2688 - loss: 2.3595
[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2733 - loss: 2.2774
[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2769 - loss: 2.2109
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2803 - loss: 2.1522
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2831 - loss: 2.1053
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step - accuracy: 0.2849 - loss: 2.0757
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 10ms/step - accuracy: 0.2849 - loss: 2.0746 - val_accuracy: 0.4702 - val_loss: 1.2447
Epoch 2/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.2740
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[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3592 - loss: 1.3649
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3520 - loss: 1.3717
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3487 - loss: 1.3736
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Epoch 3/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3843 - loss: 1.3199 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3835 - loss: 1.3202
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[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3820 - loss: 1.3185
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Epoch 4/53

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[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3920 - loss: 1.3034
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3914 - loss: 1.3010
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3906 - loss: 1.2991
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[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3913 - loss: 1.2952
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3914 - loss: 1.2941
[1m320/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3915 - loss: 1.2930
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Epoch 5/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4129 - loss: 1.2683 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4087 - loss: 1.2655
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[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4090 - loss: 1.2613
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4098 - loss: 1.2601
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4103 - loss: 1.2594
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4107 - loss: 1.2590
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4111 - loss: 1.2585
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4113 - loss: 1.2582 - val_accuracy: 0.5067 - val_loss: 1.1781
Epoch 6/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4306 - loss: 1.2452 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4227 - loss: 1.2438
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4204 - loss: 1.2446
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4207 - loss: 1.2446
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[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4208 - loss: 1.2466
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4208 - loss: 1.2466
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4211 - loss: 1.2462
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4211 - loss: 1.2460 - val_accuracy: 0.5144 - val_loss: 1.1527
Epoch 7/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4448 - loss: 1.2246 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4382 - loss: 1.2194
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[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4337 - loss: 1.2230
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4335 - loss: 1.2232
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4333 - loss: 1.2230
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4333 - loss: 1.2228 - val_accuracy: 0.5327 - val_loss: 1.1448
Epoch 8/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4524 - loss: 1.2076 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4409 - loss: 1.2149
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[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4338 - loss: 1.2185
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4332 - loss: 1.2178
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4336 - loss: 1.2171
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4339 - loss: 1.2166
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4340 - loss: 1.2161
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4341 - loss: 1.2158 - val_accuracy: 0.5186 - val_loss: 1.1571
Epoch 9/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4501 - loss: 1.2071 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4533 - loss: 1.2102
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4566 - loss: 1.2074
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4592 - loss: 1.2039
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4606 - loss: 1.2015
[1m224/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4610 - loss: 1.1993
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4608 - loss: 1.1981
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4601 - loss: 1.1976
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4595 - loss: 1.1975 - val_accuracy: 0.5214 - val_loss: 1.1374
Epoch 10/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.2500 - loss: 1.3276
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4442 - loss: 1.1997 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4466 - loss: 1.1907
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4492 - loss: 1.1868
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4490 - loss: 1.1875
[1m199/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4489 - loss: 1.1884
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4484 - loss: 1.1896
[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4484 - loss: 1.1901
[1m320/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4488 - loss: 1.1899
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4489 - loss: 1.1898 - val_accuracy: 0.5341 - val_loss: 1.1219
Epoch 11/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9710
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4632 - loss: 1.1605 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4600 - loss: 1.1699
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4612 - loss: 1.1734
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4612 - loss: 1.1755
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4603 - loss: 1.1773
[1m221/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4597 - loss: 1.1783
[1m260/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4590 - loss: 1.1792
[1m297/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4586 - loss: 1.1799
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4584 - loss: 1.1802 - val_accuracy: 0.5239 - val_loss: 1.1301
Epoch 12/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0936
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4879 - loss: 1.1529 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4724 - loss: 1.1710
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[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4673 - loss: 1.1750
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[1m260/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4654 - loss: 1.1739
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Epoch 13/53

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[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4661 - loss: 1.1809
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[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4651 - loss: 1.1744
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[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4646 - loss: 1.1721
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4647 - loss: 1.1719 - val_accuracy: 0.5281 - val_loss: 1.1216
Epoch 14/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4715 - loss: 1.1739 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4688 - loss: 1.1658
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4669 - loss: 1.1623
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4659 - loss: 1.1614
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4661 - loss: 1.1603
[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4666 - loss: 1.1596
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4673 - loss: 1.1591
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4678 - loss: 1.1591
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4680 - loss: 1.1592 - val_accuracy: 0.5474 - val_loss: 1.1091
Epoch 15/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 1.2290
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4907 - loss: 1.1712 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4852 - loss: 1.1584
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4812 - loss: 1.1549
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4799 - loss: 1.1533
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4784 - loss: 1.1530
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4777 - loss: 1.1525
[1m262/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4774 - loss: 1.1520
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4774 - loss: 1.1513
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4776 - loss: 1.1509 - val_accuracy: 0.5351 - val_loss: 1.1126
Epoch 16/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.2528
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4756 - loss: 1.1620 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4746 - loss: 1.1543
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4730 - loss: 1.1545
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4733 - loss: 1.1544
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4731 - loss: 1.1545
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4729 - loss: 1.1545
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4729 - loss: 1.1541
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4733 - loss: 1.1533
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4735 - loss: 1.1530 - val_accuracy: 0.5636 - val_loss: 1.1001
Epoch 17/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.5938 - loss: 1.0474
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4760 - loss: 1.1417 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4736 - loss: 1.1412
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[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4738 - loss: 1.1418
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4743 - loss: 1.1420
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4743 - loss: 1.1424
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4746 - loss: 1.1422
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4749 - loss: 1.1419
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4750 - loss: 1.1418 - val_accuracy: 0.5509 - val_loss: 1.0980
Epoch 18/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.5000 - loss: 1.1334
[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4786 - loss: 1.1386 
[1m 71/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4829 - loss: 1.1325
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4849 - loss: 1.1301
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4861 - loss: 1.1297
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4858 - loss: 1.1305
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4851 - loss: 1.1316
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4844 - loss: 1.1324
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4838 - loss: 1.1327
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4837 - loss: 1.1329 - val_accuracy: 0.5558 - val_loss: 1.0963
Epoch 19/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.0985
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4526 - loss: 1.1780 
[1m 71/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4620 - loss: 1.1620
[1m108/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4683 - loss: 1.1526
[1m146/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4725 - loss: 1.1460
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4744 - loss: 1.1430
[1m226/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4757 - loss: 1.1414
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4766 - loss: 1.1403
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4775 - loss: 1.1396
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4779 - loss: 1.1393 - val_accuracy: 0.5551 - val_loss: 1.0921
Epoch 20/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4062 - loss: 1.1930
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4845 - loss: 1.1290 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4903 - loss: 1.1285
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4905 - loss: 1.1326
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4915 - loss: 1.1328
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4916 - loss: 1.1320
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4917 - loss: 1.1309
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4916 - loss: 1.1298
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4915 - loss: 1.1293
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4916 - loss: 1.1289 - val_accuracy: 0.5407 - val_loss: 1.0928
Epoch 21/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6250 - loss: 0.9929
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5040 - loss: 1.1109 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4953 - loss: 1.1230
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4922 - loss: 1.1305
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4898 - loss: 1.1331
[1m199/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4893 - loss: 1.1320
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4890 - loss: 1.1307
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4889 - loss: 1.1298
[1m319/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4889 - loss: 1.1289
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4889 - loss: 1.1286 - val_accuracy: 0.5565 - val_loss: 1.0838
Epoch 22/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4375 - loss: 1.2503
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5080 - loss: 1.1211 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4983 - loss: 1.1285
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4952 - loss: 1.1279
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4940 - loss: 1.1269
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[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4932 - loss: 1.1254
[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4928 - loss: 1.1250
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Epoch 23/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4966 - loss: 1.0590 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4910 - loss: 1.0845
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[1m162/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4922 - loss: 1.1015
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[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4928 - loss: 1.1065
[1m281/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4928 - loss: 1.1080
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4929 - loss: 1.1091
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4929 - loss: 1.1095 - val_accuracy: 0.5734 - val_loss: 1.0954
Epoch 24/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5312 - loss: 1.0912
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5050 - loss: 1.0981 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5100 - loss: 1.0945
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[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5111 - loss: 1.0961
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5100 - loss: 1.0977
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5090 - loss: 1.0984
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5081 - loss: 1.0990
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5072 - loss: 1.0997
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5069 - loss: 1.1000 - val_accuracy: 0.5713 - val_loss: 1.0832
Epoch 25/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 0.9357
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5327 - loss: 1.0574 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5195 - loss: 1.0798
[1m108/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5160 - loss: 1.0882
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5145 - loss: 1.0926
[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5140 - loss: 1.0939
[1m222/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5136 - loss: 1.0949
[1m260/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5134 - loss: 1.0951
[1m296/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5130 - loss: 1.0955
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5124 - loss: 1.0961 - val_accuracy: 0.5776 - val_loss: 1.0810
Epoch 26/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0418
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5034 - loss: 1.1109 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4990 - loss: 1.1161
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4972 - loss: 1.1166
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4973 - loss: 1.1163
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4982 - loss: 1.1148
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4992 - loss: 1.1134
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5000 - loss: 1.1121
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5011 - loss: 1.1108
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5015 - loss: 1.1102 - val_accuracy: 0.5716 - val_loss: 1.0734
Epoch 27/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 26ms/step - accuracy: 0.5312 - loss: 1.0578
[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4656 - loss: 1.1195 
[1m 69/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4765 - loss: 1.1102
[1m107/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4829 - loss: 1.1053
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4861 - loss: 1.1036
[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4883 - loss: 1.1025
[1m221/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4903 - loss: 1.1015
[1m260/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4921 - loss: 1.1004
[1m296/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4934 - loss: 1.0997
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4944 - loss: 1.0993 - val_accuracy: 0.5860 - val_loss: 1.0829
Epoch 28/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1045
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4899 - loss: 1.1122 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4872 - loss: 1.1101
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4890 - loss: 1.1052
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4916 - loss: 1.1015
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4940 - loss: 1.0988
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4958 - loss: 1.0974
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4977 - loss: 1.0959
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4993 - loss: 1.0950
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4999 - loss: 1.0948 - val_accuracy: 0.5681 - val_loss: 1.0778
Epoch 29/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6875 - loss: 0.8910
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5435 - loss: 1.0647 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5307 - loss: 1.0799
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5260 - loss: 1.0854
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5234 - loss: 1.0878
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5210 - loss: 1.0887
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5195 - loss: 1.0893
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5181 - loss: 1.0896
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5171 - loss: 1.0897
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5167 - loss: 1.0896 - val_accuracy: 0.5797 - val_loss: 1.0755
Epoch 30/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4062 - loss: 1.2488
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5020 - loss: 1.0886 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5065 - loss: 1.0866
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5090 - loss: 1.0851
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5100 - loss: 1.0859
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5102 - loss: 1.0864
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5108 - loss: 1.0867
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5117 - loss: 1.0864
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5120 - loss: 1.0867
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5120 - loss: 1.0869 - val_accuracy: 0.5779 - val_loss: 1.0724
Epoch 31/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6562 - loss: 0.8832
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5237 - loss: 1.0527 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5242 - loss: 1.0691
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5238 - loss: 1.0729
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5232 - loss: 1.0743
[1m201/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5227 - loss: 1.0752
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5222 - loss: 1.0755
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5219 - loss: 1.0757
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5215 - loss: 1.0760
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5213 - loss: 1.0762 - val_accuracy: 0.5643 - val_loss: 1.0746
Epoch 32/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.1197
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5121 - loss: 1.1099 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5151 - loss: 1.0986
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5184 - loss: 1.0901
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5181 - loss: 1.0877
[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5175 - loss: 1.0869
[1m217/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5170 - loss: 1.0866
[1m254/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5166 - loss: 1.0861
[1m293/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5166 - loss: 1.0855
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5164 - loss: 1.0850 - val_accuracy: 0.5881 - val_loss: 1.0870
Epoch 33/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.3647
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4911 - loss: 1.1131 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4988 - loss: 1.0967
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4992 - loss: 1.0939
[1m163/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5004 - loss: 1.0913
[1m201/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5019 - loss: 1.0896
[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5038 - loss: 1.0874
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5057 - loss: 1.0857
[1m319/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5076 - loss: 1.0842
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5080 - loss: 1.0839 - val_accuracy: 0.5593 - val_loss: 1.0716
Epoch 34/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5082 - loss: 1.0796 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5154 - loss: 1.0756
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5176 - loss: 1.0716
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5192 - loss: 1.0702
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5198 - loss: 1.0704
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5202 - loss: 1.0712
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5205 - loss: 1.0715
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5208 - loss: 1.0716
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5208 - loss: 1.0717 - val_accuracy: 0.5832 - val_loss: 1.0597
Epoch 35/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 0.9808
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4990 - loss: 1.0946 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5077 - loss: 1.0871
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5125 - loss: 1.0832
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5145 - loss: 1.0816
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5155 - loss: 1.0806
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5163 - loss: 1.0795
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5167 - loss: 1.0788
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5169 - loss: 1.0786
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5170 - loss: 1.0785 - val_accuracy: 0.5874 - val_loss: 1.0596
Epoch 36/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6562 - loss: 0.9126
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5588 - loss: 1.0620 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5537 - loss: 1.0581
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5499 - loss: 1.0546
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5475 - loss: 1.0529
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5449 - loss: 1.0539
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5430 - loss: 1.0550
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5416 - loss: 1.0557
[1m317/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5407 - loss: 1.0560
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5405 - loss: 1.0561 - val_accuracy: 0.5920 - val_loss: 1.0588
Epoch 37/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5412 - loss: 1.0351 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5368 - loss: 1.0407
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5326 - loss: 1.0465
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5297 - loss: 1.0507
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5279 - loss: 1.0539
[1m223/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5271 - loss: 1.0562
[1m263/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5269 - loss: 1.0574
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5267 - loss: 1.0582
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5267 - loss: 1.0588 - val_accuracy: 0.5811 - val_loss: 1.0620
Epoch 38/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 0.9603
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5366 - loss: 1.0499 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5374 - loss: 1.0530
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5333 - loss: 1.0583
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5307 - loss: 1.0607
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5296 - loss: 1.0612
[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5288 - loss: 1.0617
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[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5285 - loss: 1.0614
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5286 - loss: 1.0611 - val_accuracy: 0.5902 - val_loss: 1.0533
Epoch 39/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5246 - loss: 1.0702 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5304 - loss: 1.0597
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5294 - loss: 1.0603
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5283 - loss: 1.0605
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[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5275 - loss: 1.0598
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5273 - loss: 1.0594
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5273 - loss: 1.0590
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5273 - loss: 1.0587 - val_accuracy: 0.5959 - val_loss: 1.0503
Epoch 40/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5938 - loss: 0.9954
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5514 - loss: 1.0521 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5439 - loss: 1.0593
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5424 - loss: 1.0573
[1m146/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5415 - loss: 1.0562
[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5411 - loss: 1.0554
[1m223/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5406 - loss: 1.0548
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5402 - loss: 1.0544
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5401 - loss: 1.0539
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5399 - loss: 1.0537 - val_accuracy: 0.5878 - val_loss: 1.0703
Epoch 41/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5312 - loss: 1.0995
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5211 - loss: 1.0596 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5285 - loss: 1.0576
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[1m162/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5337 - loss: 1.0516
[1m203/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5345 - loss: 1.0505
[1m244/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5346 - loss: 1.0502
[1m285/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5349 - loss: 1.0503
[1m324/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5353 - loss: 1.0505
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5354 - loss: 1.0504 - val_accuracy: 0.5997 - val_loss: 1.0635
Epoch 42/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5363 - loss: 1.0296 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5386 - loss: 1.0321
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[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5403 - loss: 1.0315
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[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5387 - loss: 1.0344
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5383 - loss: 1.0354
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5379 - loss: 1.0366
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Epoch 43/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5625 - loss: 1.1232
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5586 - loss: 1.0301 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5595 - loss: 1.0273
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[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5537 - loss: 1.0292
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5510 - loss: 1.0320
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5491 - loss: 1.0341
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5477 - loss: 1.0355
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Epoch 44/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0621
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 403ms/step
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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 16ms/step
Saved model to disk.

Accuracy capturado en la ejecución 26: 52.62 [%]
F1-score capturado en la ejecución 26: 51.78 [%]

=== EJECUCIÓN 27 ===

--- TRAIN (ejecución 27) ---

--- TEST (ejecución 27) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 14ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m68/89[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 749us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 863us/step
Global accuracy score (validation) = 58.71 [%]
Global F1 score (validation) = 57.92 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.40598413 0.27494338 0.21533191 0.1037406 ]
 [0.21124493 0.18666537 0.57019    0.03189969]
 [0.16444844 0.11560182 0.6845743  0.03537543]
 ...
 [0.23128326 0.17294046 0.544268   0.05150827]
 [0.29409295 0.14498422 0.42202225 0.1389006 ]
 [0.19949603 0.1281752  0.6387271  0.03360161]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 58.0 [%]
Global accuracy score (test) = 52.49 [%]
Global F1 score (train) = 58.22 [%]
Global F1 score (test) = 52.33 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.42      0.33      0.37       400
MODERATE-INTENSITY       0.50      0.60      0.55       400
         SEDENTARY       0.51      0.68      0.58       400
VIGOROUS-INTENSITY       0.78      0.48      0.60       345

          accuracy                           0.52      1545
         macro avg       0.55      0.52      0.52      1545
      weighted avg       0.54      0.52      0.52      1545

2025-11-05 10:56:12.232088: 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`.
2025-11-05 10:56:12.243629: 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 00:00:1762336572.256711 3093439 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762336572.260825 3093439 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762336572.270502 3093439 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336572.270525 3093439 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336572.270527 3093439 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336572.270528 3093439 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:56:12.273607: 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 00:00:1762336574.515162 3093439 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 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..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/53
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762336576.135882 3093569 service.cc:152] XLA service 0x71386c01db10 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762336576.135916 3093569 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:56:16.178048: 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 00:00:1762336576.352815 3093569 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762336578.507047 3093569 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18:21[0m 3s/step - accuracy: 0.2188 - loss: 2.6462
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2517 - loss: 2.6007  
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2665 - loss: 2.4866
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2720 - loss: 2.3861
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2751 - loss: 2.3011
[1m199/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2765 - loss: 2.2306
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2783 - loss: 2.1689
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2804 - loss: 2.1176
[1m319/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2825 - loss: 2.0715
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step - accuracy: 0.2830 - loss: 2.0619
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 10ms/step - accuracy: 0.2830 - loss: 2.0608 - val_accuracy: 0.4684 - val_loss: 1.2572
Epoch 2/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3125 - loss: 1.3459
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2992 - loss: 1.4131 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3077 - loss: 1.4017
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3142 - loss: 1.3987
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3181 - loss: 1.3953
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3209 - loss: 1.3914
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3238 - loss: 1.3869
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3266 - loss: 1.3830
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3288 - loss: 1.3799
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3297 - loss: 1.3785 - val_accuracy: 0.4649 - val_loss: 1.2502
Epoch 3/53

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[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3645 - loss: 1.3210 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3727 - loss: 1.3174
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3742 - loss: 1.3179
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3763 - loss: 1.3182
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[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3780 - loss: 1.3165
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Epoch 4/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4032 - loss: 1.2953 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4075 - loss: 1.2869
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[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4062 - loss: 1.2806
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4056 - loss: 1.2803
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Epoch 5/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4349 - loss: 1.2522 
[1m 83/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4231 - loss: 1.2512
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[1m164/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4206 - loss: 1.2492
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[1m244/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4181 - loss: 1.2503
[1m288/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4173 - loss: 1.2507
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4172 - loss: 1.2505
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4172 - loss: 1.2505 - val_accuracy: 0.5014 - val_loss: 1.1845
Epoch 6/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.3750 - loss: 1.2384
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4048 - loss: 1.2386 
[1m 71/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4111 - loss: 1.2396
[1m110/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4180 - loss: 1.2396
[1m145/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4213 - loss: 1.2398
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4247 - loss: 1.2393
[1m220/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4260 - loss: 1.2393
[1m259/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4266 - loss: 1.2396
[1m295/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4273 - loss: 1.2395
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4279 - loss: 1.2393 - val_accuracy: 0.5000 - val_loss: 1.1747
Epoch 7/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.2006
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4585 - loss: 1.2189 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4579 - loss: 1.2102
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4564 - loss: 1.2086
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4555 - loss: 1.2090
[1m199/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4529 - loss: 1.2112
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4513 - loss: 1.2124
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4502 - loss: 1.2133
[1m317/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4492 - loss: 1.2142
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4489 - loss: 1.2145 - val_accuracy: 0.5000 - val_loss: 1.1633
Epoch 8/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4590 - loss: 1.2119 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4496 - loss: 1.2172
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4485 - loss: 1.2170
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4472 - loss: 1.2161
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4458 - loss: 1.2154
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4449 - loss: 1.2143
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4443 - loss: 1.2135
[1m320/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4440 - loss: 1.2128
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4439 - loss: 1.2127 - val_accuracy: 0.5235 - val_loss: 1.1612
Epoch 9/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4062 - loss: 1.1430
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4362 - loss: 1.1809 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4384 - loss: 1.1870
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4373 - loss: 1.1912
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4354 - loss: 1.1945
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4347 - loss: 1.1969
[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4349 - loss: 1.1978
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4355 - loss: 1.1979
[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4361 - loss: 1.1980
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4362 - loss: 1.1979 - val_accuracy: 0.5176 - val_loss: 1.1555
Epoch 10/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4372 - loss: 1.1956 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4371 - loss: 1.1931
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4363 - loss: 1.1960
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4359 - loss: 1.1975
[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4364 - loss: 1.1985
[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4375 - loss: 1.1980
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4393 - loss: 1.1969
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4408 - loss: 1.1959
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4416 - loss: 1.1953 - val_accuracy: 0.5060 - val_loss: 1.1562
Epoch 11/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.2281
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4640 - loss: 1.1870 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4648 - loss: 1.1852
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4624 - loss: 1.1845
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4604 - loss: 1.1848
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4590 - loss: 1.1847
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4586 - loss: 1.1838
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4587 - loss: 1.1829
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4588 - loss: 1.1823
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4588 - loss: 1.1822 - val_accuracy: 0.5000 - val_loss: 1.1577
Epoch 12/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.1875 - loss: 1.4154
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4217 - loss: 1.2151 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4388 - loss: 1.1994
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4442 - loss: 1.1930
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4481 - loss: 1.1897
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4500 - loss: 1.1881
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4512 - loss: 1.1866
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4526 - loss: 1.1851
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4534 - loss: 1.1843
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4539 - loss: 1.1839 - val_accuracy: 0.5200 - val_loss: 1.1380
Epoch 13/53

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[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4807 - loss: 1.1856 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4718 - loss: 1.1831
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4678 - loss: 1.1832
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4658 - loss: 1.1816
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4639 - loss: 1.1810
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4628 - loss: 1.1806
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4629 - loss: 1.1795
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4634 - loss: 1.1782
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4635 - loss: 1.1778 - val_accuracy: 0.5046 - val_loss: 1.1439
Epoch 14/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4375 - loss: 1.1912
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4915 - loss: 1.1260 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4839 - loss: 1.1359
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4802 - loss: 1.1413
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4784 - loss: 1.1454
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4778 - loss: 1.1481
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4773 - loss: 1.1504
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4772 - loss: 1.1518
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4771 - loss: 1.1527
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4770 - loss: 1.1531 - val_accuracy: 0.5302 - val_loss: 1.1506
Epoch 15/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4577 - loss: 1.1774 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4649 - loss: 1.1636
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4690 - loss: 1.1596
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4721 - loss: 1.1568
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[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4742 - loss: 1.1552
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4749 - loss: 1.1551
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4754 - loss: 1.1550
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4755 - loss: 1.1551 - val_accuracy: 0.4849 - val_loss: 1.1435
Epoch 16/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 0.9553
[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4554 - loss: 1.1397 
[1m 71/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4520 - loss: 1.1506
[1m108/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4570 - loss: 1.1522
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4625 - loss: 1.1508
[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4654 - loss: 1.1501
[1m218/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4673 - loss: 1.1497
[1m255/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4691 - loss: 1.1490
[1m297/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4706 - loss: 1.1483
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4715 - loss: 1.1481 - val_accuracy: 0.5144 - val_loss: 1.1427
Epoch 17/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4688 - loss: 1.0922
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4926 - loss: 1.1277 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4899 - loss: 1.1245
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4889 - loss: 1.1255
[1m161/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4888 - loss: 1.1267
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[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4869 - loss: 1.1292
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4863 - loss: 1.1304
[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4856 - loss: 1.1321
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4854 - loss: 1.1326 - val_accuracy: 0.5060 - val_loss: 1.1330
Epoch 18/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4645 - loss: 1.1437 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4719 - loss: 1.1429
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4738 - loss: 1.1436
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4759 - loss: 1.1421
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4768 - loss: 1.1418
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4779 - loss: 1.1418
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4785 - loss: 1.1417
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4789 - loss: 1.1417
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4790 - loss: 1.1418 - val_accuracy: 0.5298 - val_loss: 1.1238
Epoch 19/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5625 - loss: 1.1884
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5045 - loss: 1.1198 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4983 - loss: 1.1194
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4940 - loss: 1.1207
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4922 - loss: 1.1215
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4908 - loss: 1.1225
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4899 - loss: 1.1235
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4892 - loss: 1.1242
[1m317/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4882 - loss: 1.1252
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4880 - loss: 1.1254 - val_accuracy: 0.5432 - val_loss: 1.1280
Epoch 20/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.5938 - loss: 1.0250
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4938 - loss: 1.1477 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4906 - loss: 1.1412
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4894 - loss: 1.1403
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4897 - loss: 1.1392
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4903 - loss: 1.1385
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4909 - loss: 1.1378
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4910 - loss: 1.1372
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4912 - loss: 1.1368
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4914 - loss: 1.1367 - val_accuracy: 0.5295 - val_loss: 1.1214
Epoch 21/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 29ms/step - accuracy: 0.3438 - loss: 1.4204
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4815 - loss: 1.1869 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4826 - loss: 1.1671
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4833 - loss: 1.1573
[1m161/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4839 - loss: 1.1534
[1m201/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4840 - loss: 1.1514
[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4848 - loss: 1.1494
[1m281/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4855 - loss: 1.1475
[1m321/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4860 - loss: 1.1458
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4860 - loss: 1.1455 - val_accuracy: 0.5291 - val_loss: 1.1140
Epoch 22/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9924
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4891 - loss: 1.1231 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4859 - loss: 1.1243
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4877 - loss: 1.1259
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4915 - loss: 1.1248
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4928 - loss: 1.1250
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4937 - loss: 1.1256
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4942 - loss: 1.1262
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4946 - loss: 1.1265
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4947 - loss: 1.1268 - val_accuracy: 0.5291 - val_loss: 1.1134
Epoch 23/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5282 - loss: 1.1161 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5152 - loss: 1.1224
[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5091 - loss: 1.1219
[1m163/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5069 - loss: 1.1209
[1m205/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5053 - loss: 1.1206
[1m246/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5043 - loss: 1.1200
[1m288/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5038 - loss: 1.1192
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5030 - loss: 1.1191
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5029 - loss: 1.1191 - val_accuracy: 0.5397 - val_loss: 1.1320
Epoch 24/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3750 - loss: 1.2355
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4858 - loss: 1.1419 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4951 - loss: 1.1287
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4989 - loss: 1.1256
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5010 - loss: 1.1243
[1m199/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5025 - loss: 1.1218
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5030 - loss: 1.1207
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5033 - loss: 1.1197
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5034 - loss: 1.1192
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5034 - loss: 1.1192 - val_accuracy: 0.5337 - val_loss: 1.1178
Epoch 25/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5065 - loss: 1.1193 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5017 - loss: 1.1201
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4986 - loss: 1.1210
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4971 - loss: 1.1208
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4967 - loss: 1.1197
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4969 - loss: 1.1187
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4971 - loss: 1.1179
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4973 - loss: 1.1173
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4974 - loss: 1.1170 - val_accuracy: 0.5534 - val_loss: 1.1080
Epoch 26/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3750 - loss: 1.1350
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4629 - loss: 1.1466 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4822 - loss: 1.1316
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4948 - loss: 1.1191
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5002 - loss: 1.1147
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5021 - loss: 1.1133
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5034 - loss: 1.1121
[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5043 - loss: 1.1110
[1m319/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5048 - loss: 1.1102
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5048 - loss: 1.1101 - val_accuracy: 0.5449 - val_loss: 1.1106
Epoch 27/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6250 - loss: 0.9275
[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5553 - loss: 1.0668 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5376 - loss: 1.0748
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5285 - loss: 1.0802
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5223 - loss: 1.0854
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5189 - loss: 1.0882
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5166 - loss: 1.0906
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5154 - loss: 1.0919
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5145 - loss: 1.0931
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5141 - loss: 1.0938 - val_accuracy: 0.5439 - val_loss: 1.0993
Epoch 28/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4926 - loss: 1.0984 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4979 - loss: 1.0965
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5016 - loss: 1.0964
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5042 - loss: 1.0971
[1m199/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5061 - loss: 1.0971
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5077 - loss: 1.0970
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5085 - loss: 1.0977
[1m319/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5088 - loss: 1.0984
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5088 - loss: 1.0985 - val_accuracy: 0.5506 - val_loss: 1.1055
Epoch 29/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.5938 - loss: 1.0699
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5350 - loss: 1.0888 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5245 - loss: 1.0875
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5219 - loss: 1.0873
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5199 - loss: 1.0890
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5183 - loss: 1.0899
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5169 - loss: 1.0907
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5161 - loss: 1.0913
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5154 - loss: 1.0920
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5151 - loss: 1.0923 - val_accuracy: 0.5583 - val_loss: 1.0933
Epoch 30/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4942 - loss: 1.1199 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5045 - loss: 1.1048
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5073 - loss: 1.1003
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5083 - loss: 1.0971
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5096 - loss: 1.0954
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5100 - loss: 1.0953
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5102 - loss: 1.0952
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5105 - loss: 1.0947
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5105 - loss: 1.0946 - val_accuracy: 0.5667 - val_loss: 1.0883
Epoch 31/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6250 - loss: 0.9447
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5277 - loss: 1.0973 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5243 - loss: 1.0924
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5205 - loss: 1.0934
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5162 - loss: 1.0950
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5127 - loss: 1.0967
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5106 - loss: 1.0977
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5096 - loss: 1.0975
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5094 - loss: 1.0968
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5094 - loss: 1.0965 - val_accuracy: 0.5323 - val_loss: 1.1221
Epoch 32/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6562 - loss: 0.9758
[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5405 - loss: 1.0556 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5338 - loss: 1.0657
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5299 - loss: 1.0700
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5281 - loss: 1.0721
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5271 - loss: 1.0734
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5260 - loss: 1.0746
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5248 - loss: 1.0758
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5237 - loss: 1.0773
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5233 - loss: 1.0778 - val_accuracy: 0.5748 - val_loss: 1.1100
Epoch 33/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5316 - loss: 1.0860 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5207 - loss: 1.0949
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5170 - loss: 1.0971
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5145 - loss: 1.0976
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5133 - loss: 1.0972
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5135 - loss: 1.0960
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5141 - loss: 1.0947
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5143 - loss: 1.0936
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5144 - loss: 1.0930 - val_accuracy: 0.5643 - val_loss: 1.1036
Epoch 34/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3438 - loss: 1.3484
[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4819 - loss: 1.0960 
[1m 67/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4936 - loss: 1.0899
[1m104/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4978 - loss: 1.0869
[1m141/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5021 - loss: 1.0847
[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5045 - loss: 1.0837
[1m217/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5062 - loss: 1.0840
[1m259/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5077 - loss: 1.0845
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5087 - loss: 1.0849
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5095 - loss: 1.0848 - val_accuracy: 0.5688 - val_loss: 1.0951
Epoch 35/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 27ms/step - accuracy: 0.7500 - loss: 0.8948
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[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5259 - loss: 1.0555
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[1m319/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5248 - loss: 1.0664
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5247 - loss: 1.0667 - val_accuracy: 0.5502 - val_loss: 1.0920

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 398ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 17ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 17ms/step
Saved model to disk.

Accuracy capturado en la ejecución 27: 52.49 [%]
F1-score capturado en la ejecución 27: 52.33 [%]

=== EJECUCIÓN 28 ===

--- TRAIN (ejecución 28) ---

--- TEST (ejecución 28) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:28[0m 1s/step
[1m 67/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 760us/step
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 14ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m72/89[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 712us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 839us/step
Global accuracy score (validation) = 55.2 [%]
Global F1 score (validation) = 53.31 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.3048123  0.42421204 0.10494791 0.1660278 ]
 [0.13978173 0.1322525  0.7069673  0.02099848]
 [0.18109454 0.14342013 0.6129416  0.06254374]
 ...
 [0.20577283 0.1699721  0.56029975 0.06395536]
 [0.2480142  0.15243402 0.48723716 0.11231469]
 [0.14256021 0.13863319 0.6905236  0.02828294]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 55.37 [%]
Global accuracy score (test) = 50.49 [%]
Global F1 score (train) = 54.68 [%]
Global F1 score (test) = 49.36 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.39      0.20      0.27       400
MODERATE-INTENSITY       0.44      0.66      0.53       400
         SEDENTARY       0.51      0.68      0.58       400
VIGOROUS-INTENSITY       0.81      0.47      0.60       345

          accuracy                           0.50      1545
         macro avg       0.54      0.50      0.49      1545
      weighted avg       0.53      0.50      0.49      1545

2025-11-05 10:56:52.403657: 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`.
2025-11-05 10:56:52.414729: 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 00:00:1762336612.427665 3097668 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762336612.431744 3097668 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762336612.441367 3097668 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336612.441383 3097668 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336612.441385 3097668 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336612.441386 3097668 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:56:52.444530: 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 00:00:1762336614.649263 3097668 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 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..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/53
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762336616.268623 3097798 service.cc:152] XLA service 0x7ded44004860 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762336616.268679 3097798 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:56:56.307982: 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 00:00:1762336616.471101 3097798 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762336618.651494 3097798 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18:22[0m 3s/step - accuracy: 0.3438 - loss: 2.2840
[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2762 - loss: 2.5569  
[1m 69/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2828 - loss: 2.3978
[1m107/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2839 - loss: 2.2857
[1m145/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2856 - loss: 2.1967
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2878 - loss: 2.1200
[1m226/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2906 - loss: 2.0550
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2929 - loss: 2.0013
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2949 - loss: 1.9576
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step - accuracy: 0.2959 - loss: 1.9373
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 10ms/step - accuracy: 0.2960 - loss: 1.9364 - val_accuracy: 0.4544 - val_loss: 1.2794
Epoch 2/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3169 - loss: 1.3798 
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[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3306 - loss: 1.3649
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3357 - loss: 1.3609
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3392 - loss: 1.3578
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3417 - loss: 1.3556
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3443 - loss: 1.3531
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3464 - loss: 1.3512
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Epoch 3/53

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[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4039 - loss: 1.2739
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3987 - loss: 1.2789
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3952 - loss: 1.2832
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[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3908 - loss: 1.2892
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Epoch 4/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4238 - loss: 1.2775 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4018 - loss: 1.2877
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3947 - loss: 1.2902
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3917 - loss: 1.2910
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[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3938 - loss: 1.2856
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Epoch 5/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3886 - loss: 1.2591 
[1m 73/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3948 - loss: 1.2573
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4022 - loss: 1.2528
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4065 - loss: 1.2511
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4085 - loss: 1.2505
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4099 - loss: 1.2498
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4107 - loss: 1.2493
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4113 - loss: 1.2490
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4116 - loss: 1.2489 - val_accuracy: 0.4853 - val_loss: 1.1917
Epoch 6/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3901 - loss: 1.2867 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4082 - loss: 1.2625
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4140 - loss: 1.2529
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4159 - loss: 1.2487
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4172 - loss: 1.2472
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4174 - loss: 1.2471
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4173 - loss: 1.2474
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4175 - loss: 1.2472
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4176 - loss: 1.2470 - val_accuracy: 0.4958 - val_loss: 1.1775
Epoch 7/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.3750 - loss: 1.2567
[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4288 - loss: 1.2278 
[1m 71/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4385 - loss: 1.2227
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[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4403 - loss: 1.2179
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4404 - loss: 1.2169
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4395 - loss: 1.2174
[1m262/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4388 - loss: 1.2179
[1m297/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4384 - loss: 1.2184
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4380 - loss: 1.2190 - val_accuracy: 0.4909 - val_loss: 1.1734
Epoch 8/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4403 - loss: 1.2138 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4426 - loss: 1.2144
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4445 - loss: 1.2136
[1m161/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4458 - loss: 1.2133
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4456 - loss: 1.2137
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4458 - loss: 1.2136
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4461 - loss: 1.2132
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4464 - loss: 1.2129
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4465 - loss: 1.2128 - val_accuracy: 0.5109 - val_loss: 1.1568
Epoch 9/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4252 - loss: 1.2019 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4405 - loss: 1.1888
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4436 - loss: 1.1910
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4458 - loss: 1.1943
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4468 - loss: 1.1963
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4477 - loss: 1.1974
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4485 - loss: 1.1977
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4492 - loss: 1.1979
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4494 - loss: 1.1980 - val_accuracy: 0.4940 - val_loss: 1.1518
Epoch 10/53

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[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4765 - loss: 1.1582 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4667 - loss: 1.1734
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4613 - loss: 1.1798
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4598 - loss: 1.1818
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4592 - loss: 1.1818
[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4594 - loss: 1.1813
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4592 - loss: 1.1815
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4587 - loss: 1.1821
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4586 - loss: 1.1824 - val_accuracy: 0.5109 - val_loss: 1.1436
Epoch 11/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3438 - loss: 1.3439
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4250 - loss: 1.2242 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4352 - loss: 1.2095
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4413 - loss: 1.2027
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4462 - loss: 1.1974
[1m194/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4495 - loss: 1.1943
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4513 - loss: 1.1925
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4526 - loss: 1.1914
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4538 - loss: 1.1901
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4544 - loss: 1.1896 - val_accuracy: 0.5204 - val_loss: 1.1386
Epoch 12/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4062 - loss: 1.1739
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4821 - loss: 1.1383 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4771 - loss: 1.1537
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4740 - loss: 1.1598
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4732 - loss: 1.1641
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4740 - loss: 1.1666
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4742 - loss: 1.1681
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4743 - loss: 1.1690
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4741 - loss: 1.1698
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4740 - loss: 1.1703 - val_accuracy: 0.5151 - val_loss: 1.1252
Epoch 13/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4996 - loss: 1.1393 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4991 - loss: 1.1359
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4946 - loss: 1.1409
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4918 - loss: 1.1440
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4898 - loss: 1.1466
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4876 - loss: 1.1493
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4854 - loss: 1.1520
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4840 - loss: 1.1538
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4834 - loss: 1.1546 - val_accuracy: 0.5225 - val_loss: 1.1448
Epoch 14/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4641 - loss: 1.1781 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4661 - loss: 1.1693
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4692 - loss: 1.1655
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4719 - loss: 1.1634
[1m199/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4730 - loss: 1.1616
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4742 - loss: 1.1605
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4748 - loss: 1.1603
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4752 - loss: 1.1602
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4754 - loss: 1.1602 - val_accuracy: 0.5197 - val_loss: 1.1339
Epoch 15/53

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[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4342 - loss: 1.1867 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4448 - loss: 1.1756
[1m110/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4506 - loss: 1.1732
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4555 - loss: 1.1714
[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4587 - loss: 1.1700
[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4608 - loss: 1.1687
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4626 - loss: 1.1679
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4644 - loss: 1.1673
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4654 - loss: 1.1670 - val_accuracy: 0.5204 - val_loss: 1.1323
Epoch 16/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4674 - loss: 1.1560 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4705 - loss: 1.1604
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4704 - loss: 1.1616
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4703 - loss: 1.1613
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4704 - loss: 1.1610
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4712 - loss: 1.1599
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4718 - loss: 1.1587
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4724 - loss: 1.1577
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4729 - loss: 1.1570 - val_accuracy: 0.5256 - val_loss: 1.1258
Epoch 17/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4525 - loss: 1.1774 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4555 - loss: 1.1654
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4613 - loss: 1.1576
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4657 - loss: 1.1536
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[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4714 - loss: 1.1497
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4729 - loss: 1.1489
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4739 - loss: 1.1484
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Epoch 18/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5137 - loss: 1.1416 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5006 - loss: 1.1477
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4953 - loss: 1.1482
[1m161/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4922 - loss: 1.1490
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4911 - loss: 1.1488
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4905 - loss: 1.1481
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4902 - loss: 1.1472
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4902 - loss: 1.1462
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4901 - loss: 1.1459 - val_accuracy: 0.5302 - val_loss: 1.1327
Epoch 19/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.0598
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5046 - loss: 1.1338 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5050 - loss: 1.1284
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5026 - loss: 1.1272
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5009 - loss: 1.1281
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4999 - loss: 1.1290
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4988 - loss: 1.1301
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4979 - loss: 1.1310
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4970 - loss: 1.1319
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4967 - loss: 1.1323 - val_accuracy: 0.5355 - val_loss: 1.1148
Epoch 20/53

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5370 - loss: 1.0634 
[1m 68/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5289 - loss: 1.0727
[1m106/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5231 - loss: 1.0815
[1m143/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5189 - loss: 1.0873
[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5152 - loss: 1.0924
[1m218/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5126 - loss: 1.0965
[1m258/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5101 - loss: 1.1006
[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5077 - loss: 1.1044
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5060 - loss: 1.1070 - val_accuracy: 0.5411 - val_loss: 1.1264
Epoch 21/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.3125 - loss: 1.3711
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5019 - loss: 1.1079 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4988 - loss: 1.1138
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4967 - loss: 1.1174
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4947 - loss: 1.1209
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4946 - loss: 1.1214
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4945 - loss: 1.1223
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4942 - loss: 1.1234
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4938 - loss: 1.1243
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4938 - loss: 1.1244 - val_accuracy: 0.5358 - val_loss: 1.1208
Epoch 22/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.5625 - loss: 1.0734
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5065 - loss: 1.1036 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5078 - loss: 1.1143
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5065 - loss: 1.1175
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5046 - loss: 1.1203
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5032 - loss: 1.1218
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5013 - loss: 1.1230
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5004 - loss: 1.1232
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4997 - loss: 1.1233
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4995 - loss: 1.1233 - val_accuracy: 0.5502 - val_loss: 1.1038
Epoch 23/53

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[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5436 - loss: 1.0702 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5291 - loss: 1.0873
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5220 - loss: 1.0944
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5171 - loss: 1.1000
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5142 - loss: 1.1040
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5120 - loss: 1.1069
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5104 - loss: 1.1091
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5092 - loss: 1.1107
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5087 - loss: 1.1114 - val_accuracy: 0.5386 - val_loss: 1.1154
Epoch 24/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 36ms/step - accuracy: 0.5312 - loss: 1.0474
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5157 - loss: 1.1064  
[1m 70/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5141 - loss: 1.1129
[1m106/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5123 - loss: 1.1115
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5115 - loss: 1.1110
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5114 - loss: 1.1109
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5107 - loss: 1.1115
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5099 - loss: 1.1125
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5093 - loss: 1.1131
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5091 - loss: 1.1133 - val_accuracy: 0.5530 - val_loss: 1.0973
Epoch 25/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.7500 - loss: 0.9047
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5226 - loss: 1.1051 
[1m 82/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5193 - loss: 1.1056
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5184 - loss: 1.1049
[1m162/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5166 - loss: 1.1049
[1m203/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5148 - loss: 1.1060
[1m243/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5134 - loss: 1.1076
[1m285/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5126 - loss: 1.1085
[1m325/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5120 - loss: 1.1091
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5120 - loss: 1.1091 - val_accuracy: 0.5492 - val_loss: 1.1186
Epoch 26/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6875 - loss: 1.0116
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5244 - loss: 1.1345 
[1m 70/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5143 - loss: 1.1305
[1m108/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5109 - loss: 1.1227
[1m145/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5100 - loss: 1.1178
[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5103 - loss: 1.1136
[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5101 - loss: 1.1113
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5098 - loss: 1.1100
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5094 - loss: 1.1092
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5094 - loss: 1.1087 - val_accuracy: 0.5460 - val_loss: 1.1138
Epoch 27/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4062 - loss: 1.1870
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5050 - loss: 1.1002 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5054 - loss: 1.0960
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5056 - loss: 1.0949
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5067 - loss: 1.0945
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5085 - loss: 1.0935
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5094 - loss: 1.0937
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5101 - loss: 1.0942
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5108 - loss: 1.0947
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5112 - loss: 1.0950 - val_accuracy: 0.5590 - val_loss: 1.1143
Epoch 28/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5135 - loss: 1.0775 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5156 - loss: 1.0842
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5171 - loss: 1.0883
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5180 - loss: 1.0903
[1m200/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5171 - loss: 1.0929
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5166 - loss: 1.0941
[1m281/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5167 - loss: 1.0949
[1m321/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5169 - loss: 1.0955
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5170 - loss: 1.0956 - val_accuracy: 0.5636 - val_loss: 1.0941
Epoch 29/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.2201
[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4582 - loss: 1.1633 
[1m 69/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4716 - loss: 1.1431
[1m107/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4811 - loss: 1.1326
[1m142/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4879 - loss: 1.1263
[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4928 - loss: 1.1212
[1m223/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4964 - loss: 1.1168
[1m260/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4988 - loss: 1.1139
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5009 - loss: 1.1112
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5022 - loss: 1.1096 - val_accuracy: 0.5555 - val_loss: 1.1031
Epoch 30/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6250 - loss: 0.8892
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5075 - loss: 1.1091 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5046 - loss: 1.1101
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5044 - loss: 1.1088
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5043 - loss: 1.1071
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5057 - loss: 1.1043
[1m224/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5068 - loss: 1.1024
[1m262/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5076 - loss: 1.1006
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5082 - loss: 1.0993
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5084 - loss: 1.0986 - val_accuracy: 0.5586 - val_loss: 1.1004
Epoch 31/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 19ms/step - accuracy: 0.3438 - loss: 1.3062
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5313 - loss: 1.0675 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5309 - loss: 1.0710
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5278 - loss: 1.0748
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5262 - loss: 1.0769
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5257 - loss: 1.0776
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5250 - loss: 1.0784
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5245 - loss: 1.0788
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5238 - loss: 1.0793
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5235 - loss: 1.0795 - val_accuracy: 0.5593 - val_loss: 1.0978
Epoch 32/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.1864
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5053 - loss: 1.1040 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5162 - loss: 1.0957
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5201 - loss: 1.0912
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5208 - loss: 1.0890
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5220 - loss: 1.0867
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5224 - loss: 1.0854
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5230 - loss: 1.0843
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5231 - loss: 1.0838
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5232 - loss: 1.0835 - val_accuracy: 0.5421 - val_loss: 1.0960
Epoch 33/53

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[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5298 - loss: 1.0639 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5255 - loss: 1.0707
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5262 - loss: 1.0709
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5282 - loss: 1.0694
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[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5298 - loss: 1.0694
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5297 - loss: 1.0703
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5290 - loss: 1.0716
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5287 - loss: 1.0722 - val_accuracy: 0.5713 - val_loss: 1.0919
Epoch 34/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5081 - loss: 1.1011 
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5119 - loss: 1.0891
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[1m162/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5174 - loss: 1.0773
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[1m245/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5196 - loss: 1.0735
[1m285/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5196 - loss: 1.0732
[1m325/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5197 - loss: 1.0730
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5198 - loss: 1.0730 - val_accuracy: 0.5699 - val_loss: 1.0829
Epoch 35/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5315 - loss: 1.0379 
[1m 83/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5271 - loss: 1.0545
[1m125/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5248 - loss: 1.0610
[1m166/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5254 - loss: 1.0633
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[1m248/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5258 - loss: 1.0666
[1m286/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5259 - loss: 1.0677
[1m327/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5259 - loss: 1.0684
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5259 - loss: 1.0684 - val_accuracy: 0.5600 - val_loss: 1.1009
Epoch 36/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.4688 - loss: 1.2031
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5246 - loss: 1.0924 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5268 - loss: 1.0843
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5241 - loss: 1.0829
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5238 - loss: 1.0804
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5242 - loss: 1.0790
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5244 - loss: 1.0778
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5244 - loss: 1.0771
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5248 - loss: 1.0762
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5250 - loss: 1.0759 - val_accuracy: 0.5636 - val_loss: 1.0884
Epoch 37/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 1.0246
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5547 - loss: 1.0516 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5428 - loss: 1.0602
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[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5360 - loss: 1.0637
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[1m260/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5355 - loss: 1.0625
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5354 - loss: 1.0622
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5351 - loss: 1.0624 - val_accuracy: 0.5579 - val_loss: 1.0992
Epoch 38/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6250 - loss: 0.9875
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5357 - loss: 1.0257 
[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5385 - loss: 1.0376
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5388 - loss: 1.0420
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5388 - loss: 1.0446
[1m200/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5375 - loss: 1.0477
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5367 - loss: 1.0501
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5364 - loss: 1.0513
[1m321/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5364 - loss: 1.0520
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5365 - loss: 1.0521 - val_accuracy: 0.5734 - val_loss: 1.0838
Epoch 39/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5938 - loss: 1.0761
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5229 - loss: 1.0774 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5222 - loss: 1.0741
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5215 - loss: 1.0738
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5219 - loss: 1.0744
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5224 - loss: 1.0742
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5239 - loss: 1.0730
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5254 - loss: 1.0715
[1m317/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5266 - loss: 1.0703
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5270 - loss: 1.0699 - val_accuracy: 0.5674 - val_loss: 1.0906

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 400ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 15ms/step
Saved model to disk.

Accuracy capturado en la ejecución 28: 50.49 [%]
F1-score capturado en la ejecución 28: 49.36 [%]

=== EJECUCIÓN 29 ===

--- TRAIN (ejecución 29) ---

--- TEST (ejecución 29) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:39[0m 1s/step
[1m 57/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 894us/step
[1m130/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 777us/step
[1m200/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 757us/step
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 742us/step
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 15ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m64/89[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 797us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 924us/step
Global accuracy score (validation) = 57.51 [%]
Global F1 score (validation) = 56.22 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.24407959 0.19395237 0.5260129  0.03595523]
 [0.06759105 0.09033699 0.8322486  0.00982324]
 [0.2473227  0.19384651 0.5215947  0.0372361 ]
 ...
 [0.17118368 0.12172936 0.6390893  0.06799763]
 [0.22790092 0.10840632 0.5174398  0.14625299]
 [0.18972474 0.11521658 0.6344404  0.06061825]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 57.6 [%]
Global accuracy score (test) = 51.26 [%]
Global F1 score (train) = 57.4 [%]
Global F1 score (test) = 50.78 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.38      0.28      0.32       400
MODERATE-INTENSITY       0.50      0.54      0.52       400
         SEDENTARY       0.50      0.73      0.59       400
VIGOROUS-INTENSITY       0.75      0.51      0.60       345

          accuracy                           0.51      1545
         macro avg       0.53      0.51      0.51      1545
      weighted avg       0.52      0.51      0.50      1545

2025-11-05 10:57:35.249258: 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`.
2025-11-05 10:57:35.260683: 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 00:00:1762336655.273929 3102291 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762336655.278030 3102291 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762336655.287919 3102291 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336655.287934 3102291 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336655.287936 3102291 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762336655.287937 3102291 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 10:57:35.291061: 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 00:00:1762336657.506991 3102291 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 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..
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/53
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762336659.136226 3102401 service.cc:152] XLA service 0x7d248400c920 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762336659.136258 3102401 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 10:57:39.176168: 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 00:00:1762336659.340010 3102401 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762336661.531795 3102401 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 2/53

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Epoch 3/53

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Epoch 4/53

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Epoch 5/53

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[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4164 - loss: 1.2421
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[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4152 - loss: 1.2503
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Epoch 6/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4496 - loss: 1.2346 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4474 - loss: 1.2328
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4469 - loss: 1.2319
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4447 - loss: 1.2320
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[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4405 - loss: 1.2335
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4393 - loss: 1.2336
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4383 - loss: 1.2338
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4377 - loss: 1.2338 - val_accuracy: 0.5028 - val_loss: 1.1790
Epoch 7/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4439 - loss: 1.2579 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4421 - loss: 1.2478
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4419 - loss: 1.2401
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4420 - loss: 1.2349
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4425 - loss: 1.2312
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4426 - loss: 1.2292
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4432 - loss: 1.2271
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4437 - loss: 1.2252
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4438 - loss: 1.2245 - val_accuracy: 0.4723 - val_loss: 1.1614
Epoch 8/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4030 - loss: 1.2559 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4196 - loss: 1.2352
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4268 - loss: 1.2308
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4302 - loss: 1.2277
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[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4348 - loss: 1.2229
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4366 - loss: 1.2207
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4376 - loss: 1.2193
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4381 - loss: 1.2186 - val_accuracy: 0.5119 - val_loss: 1.1585
Epoch 9/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4466 - loss: 1.2441 
[1m 83/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4557 - loss: 1.2146
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4561 - loss: 1.2076
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4566 - loss: 1.2047
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[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4569 - loss: 1.2013
[1m282/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4569 - loss: 1.2005
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Epoch 10/53

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[1m 79/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4651 - loss: 1.1700
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[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4588 - loss: 1.1818
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[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4575 - loss: 1.1846
[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4573 - loss: 1.1850
[1m323/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4574 - loss: 1.1853
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4574 - loss: 1.1853 - val_accuracy: 0.5225 - val_loss: 1.1367
Epoch 11/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4791 - loss: 1.1469 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4876 - loss: 1.1509
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[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4834 - loss: 1.1600
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4803 - loss: 1.1640
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4777 - loss: 1.1668
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4752 - loss: 1.1691
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4735 - loss: 1.1707
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4729 - loss: 1.1711 - val_accuracy: 0.5204 - val_loss: 1.1335
Epoch 12/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4062 - loss: 1.5473
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4696 - loss: 1.2222 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4663 - loss: 1.2042
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4639 - loss: 1.1983
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4636 - loss: 1.1939
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4637 - loss: 1.1908
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4638 - loss: 1.1886
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4637 - loss: 1.1875
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4640 - loss: 1.1864
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4641 - loss: 1.1860 - val_accuracy: 0.5400 - val_loss: 1.1269
Epoch 13/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4062 - loss: 1.2121
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4630 - loss: 1.1789 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4684 - loss: 1.1775
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4674 - loss: 1.1779
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4668 - loss: 1.1771
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4662 - loss: 1.1771
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4663 - loss: 1.1764
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4664 - loss: 1.1753
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4663 - loss: 1.1746
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4664 - loss: 1.1743 - val_accuracy: 0.5460 - val_loss: 1.1237
Epoch 14/53

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[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4662 - loss: 1.1616 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4621 - loss: 1.1646
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4627 - loss: 1.1629
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4635 - loss: 1.1631
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[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4651 - loss: 1.1619
[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4656 - loss: 1.1615
[1m322/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4658 - loss: 1.1612
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4659 - loss: 1.1612 - val_accuracy: 0.5481 - val_loss: 1.1192
Epoch 15/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.0615
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4752 - loss: 1.1209 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4743 - loss: 1.1345
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4769 - loss: 1.1387
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4771 - loss: 1.1428
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4766 - loss: 1.1458
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4763 - loss: 1.1478
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4757 - loss: 1.1494
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4755 - loss: 1.1502
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4753 - loss: 1.1506 - val_accuracy: 0.5435 - val_loss: 1.1284
Epoch 16/53

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[1m 42/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4988 - loss: 1.1062 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4935 - loss: 1.1181
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[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4871 - loss: 1.1257
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4855 - loss: 1.1283
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4849 - loss: 1.1302
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4845 - loss: 1.1315
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4840 - loss: 1.1333
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4838 - loss: 1.1341 - val_accuracy: 0.5516 - val_loss: 1.1359
Epoch 17/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5312 - loss: 1.1500
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4917 - loss: 1.1449 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4895 - loss: 1.1448
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4890 - loss: 1.1449
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4879 - loss: 1.1451
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4868 - loss: 1.1449
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4856 - loss: 1.1457
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4851 - loss: 1.1461
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4847 - loss: 1.1461
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4846 - loss: 1.1461 - val_accuracy: 0.5239 - val_loss: 1.1280
Epoch 18/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1663
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4624 - loss: 1.1593 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4692 - loss: 1.1621
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4753 - loss: 1.1589
[1m146/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4775 - loss: 1.1572
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4793 - loss: 1.1549
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4808 - loss: 1.1520
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4821 - loss: 1.1497
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4832 - loss: 1.1480
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4835 - loss: 1.1475 - val_accuracy: 0.5463 - val_loss: 1.1117
Epoch 19/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4841 - loss: 1.1447 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4876 - loss: 1.1459
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4894 - loss: 1.1465
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4885 - loss: 1.1478
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4882 - loss: 1.1479
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4883 - loss: 1.1472
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4887 - loss: 1.1464
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4891 - loss: 1.1454
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4893 - loss: 1.1449 - val_accuracy: 0.5446 - val_loss: 1.1330
Epoch 20/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.2611
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4482 - loss: 1.1645 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4580 - loss: 1.1578
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4645 - loss: 1.1543
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4688 - loss: 1.1515
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4715 - loss: 1.1496
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4738 - loss: 1.1484
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4755 - loss: 1.1471
[1m320/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4767 - loss: 1.1460
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4770 - loss: 1.1457 - val_accuracy: 0.5439 - val_loss: 1.1038
Epoch 21/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 1.0643
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4832 - loss: 1.1204 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4895 - loss: 1.1198
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4931 - loss: 1.1184
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4954 - loss: 1.1176
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4966 - loss: 1.1174
[1m226/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4974 - loss: 1.1180
[1m263/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4980 - loss: 1.1185
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4981 - loss: 1.1191
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4979 - loss: 1.1196 - val_accuracy: 0.5414 - val_loss: 1.1207
Epoch 22/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.2251
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4972 - loss: 1.1351 
[1m 78/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4892 - loss: 1.1374
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4863 - loss: 1.1373
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4861 - loss: 1.1354
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4862 - loss: 1.1343
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4868 - loss: 1.1327
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4877 - loss: 1.1308
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4885 - loss: 1.1294
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4887 - loss: 1.1291 - val_accuracy: 0.5428 - val_loss: 1.1169
Epoch 23/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 1.0185
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5211 - loss: 1.0793 
[1m 71/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5224 - loss: 1.0788
[1m110/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5209 - loss: 1.0828
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5182 - loss: 1.0879
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5163 - loss: 1.0918
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5149 - loss: 1.0949
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5131 - loss: 1.0978
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5117 - loss: 1.1000
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5111 - loss: 1.1011 - val_accuracy: 0.5611 - val_loss: 1.1059
Epoch 24/53

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[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4753 - loss: 1.1302 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4838 - loss: 1.1220
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4852 - loss: 1.1219
[1m144/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4866 - loss: 1.1225
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4893 - loss: 1.1210
[1m223/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4919 - loss: 1.1187
[1m263/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4940 - loss: 1.1168
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4954 - loss: 1.1158
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4961 - loss: 1.1152 - val_accuracy: 0.5832 - val_loss: 1.1042
Epoch 25/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.0513
[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5003 - loss: 1.1178 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5111 - loss: 1.1025
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5122 - loss: 1.1011
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5111 - loss: 1.1016
[1m198/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5104 - loss: 1.1019
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5096 - loss: 1.1025
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5090 - loss: 1.1031
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5085 - loss: 1.1039
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5084 - loss: 1.1042 - val_accuracy: 0.5618 - val_loss: 1.0964
Epoch 26/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 19ms/step - accuracy: 0.6562 - loss: 0.8929
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5241 - loss: 1.0634 
[1m 72/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5154 - loss: 1.0855
[1m109/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5127 - loss: 1.0942
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5126 - loss: 1.0969
[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5136 - loss: 1.0976
[1m221/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5144 - loss: 1.0983
[1m261/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5141 - loss: 1.0992
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5133 - loss: 1.1002
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5128 - loss: 1.1009 - val_accuracy: 0.5678 - val_loss: 1.0963
Epoch 27/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0909
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4828 - loss: 1.1237 
[1m 82/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4930 - loss: 1.1172
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4975 - loss: 1.1144
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4995 - loss: 1.1134
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5007 - loss: 1.1123
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5013 - loss: 1.1116
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5017 - loss: 1.1108
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5022 - loss: 1.1102
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5024 - loss: 1.1098 - val_accuracy: 0.5874 - val_loss: 1.0960
Epoch 28/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5312 - loss: 1.0817
[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5123 - loss: 1.0666 
[1m 68/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5051 - loss: 1.0855
[1m107/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5052 - loss: 1.0899
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5071 - loss: 1.0913
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5079 - loss: 1.0916
[1m227/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5084 - loss: 1.0924
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5084 - loss: 1.0934
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5080 - loss: 1.0945
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5078 - loss: 1.0950 - val_accuracy: 0.5558 - val_loss: 1.1024
Epoch 29/53

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[1m 36/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5353 - loss: 1.0836 
[1m 77/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5291 - loss: 1.0872
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5251 - loss: 1.0896
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5220 - loss: 1.0902
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5207 - loss: 1.0896
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5198 - loss: 1.0893
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5196 - loss: 1.0890
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5192 - loss: 1.0887
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5190 - loss: 1.0886 - val_accuracy: 0.5709 - val_loss: 1.0978
Epoch 30/53

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[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5047 - loss: 1.0922 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5122 - loss: 1.0983
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5142 - loss: 1.0960
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5147 - loss: 1.0942
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5152 - loss: 1.0924
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5153 - loss: 1.0921
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5152 - loss: 1.0924
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5151 - loss: 1.0926
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5153 - loss: 1.0924 - val_accuracy: 0.5660 - val_loss: 1.0954
Epoch 31/53

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[1m 40/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5218 - loss: 1.1222 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5193 - loss: 1.1164
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5185 - loss: 1.1124
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5172 - loss: 1.1104
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[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5180 - loss: 1.1053
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5182 - loss: 1.1032
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5182 - loss: 1.1021
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5182 - loss: 1.1017 - val_accuracy: 0.5734 - val_loss: 1.0933
Epoch 32/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9229
[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5286 - loss: 1.0906 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5265 - loss: 1.0910
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5240 - loss: 1.0911
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5230 - loss: 1.0906
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5225 - loss: 1.0897
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5224 - loss: 1.0891
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5225 - loss: 1.0884
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5226 - loss: 1.0881
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5226 - loss: 1.0880 - val_accuracy: 0.5737 - val_loss: 1.0946
Epoch 33/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.1438
[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5525 - loss: 1.0663 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5405 - loss: 1.0674
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5354 - loss: 1.0684
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5325 - loss: 1.0699
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5311 - loss: 1.0708
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5302 - loss: 1.0716
[1m263/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5298 - loss: 1.0720
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5296 - loss: 1.0724
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5294 - loss: 1.0724 - val_accuracy: 0.5593 - val_loss: 1.1052
Epoch 34/53

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[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5331 - loss: 1.0797 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5348 - loss: 1.0749
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5362 - loss: 1.0707
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5362 - loss: 1.0691
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5356 - loss: 1.0686
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5350 - loss: 1.0683
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5345 - loss: 1.0684
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5334 - loss: 1.0688
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5329 - loss: 1.0691 - val_accuracy: 0.5874 - val_loss: 1.0828
Epoch 35/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 1.2678
[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5163 - loss: 1.1102 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5253 - loss: 1.0939
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5242 - loss: 1.0882
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5231 - loss: 1.0846
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5231 - loss: 1.0821
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5238 - loss: 1.0798
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5242 - loss: 1.0784
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5244 - loss: 1.0774
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5244 - loss: 1.0772 - val_accuracy: 0.5755 - val_loss: 1.0950
Epoch 36/53

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[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5336 - loss: 1.0576 
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5398 - loss: 1.0593
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5415 - loss: 1.0580
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5410 - loss: 1.0596
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5410 - loss: 1.0595
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5398 - loss: 1.0600
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5390 - loss: 1.0603
[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5383 - loss: 1.0606
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5382 - loss: 1.0607 - val_accuracy: 0.5758 - val_loss: 1.0811
Epoch 37/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.0639
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4999 - loss: 1.0945 
[1m 75/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5048 - loss: 1.0908
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5089 - loss: 1.0867
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5130 - loss: 1.0826
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5155 - loss: 1.0793
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5176 - loss: 1.0767
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5191 - loss: 1.0748
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5200 - loss: 1.0735
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5205 - loss: 1.0729 - val_accuracy: 0.5860 - val_loss: 1.0863
Epoch 38/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 1.0982
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5597 - loss: 1.0639 
[1m 76/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5527 - loss: 1.0615
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5481 - loss: 1.0631
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5461 - loss: 1.0629
[1m195/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5452 - loss: 1.0619
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5435 - loss: 1.0625
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5419 - loss: 1.0632
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5407 - loss: 1.0637
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5403 - loss: 1.0639 - val_accuracy: 0.5636 - val_loss: 1.1004
Epoch 39/53

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[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5442 - loss: 1.0538 
[1m 83/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5467 - loss: 1.0473
[1m126/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5475 - loss: 1.0476
[1m167/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5470 - loss: 1.0484
[1m205/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5470 - loss: 1.0476
[1m243/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5468 - loss: 1.0475
[1m283/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5469 - loss: 1.0474
[1m321/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5467 - loss: 1.0478
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5466 - loss: 1.0479 - val_accuracy: 0.5871 - val_loss: 1.0894
Epoch 40/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3750 - loss: 1.4423
[1m 37/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5196 - loss: 1.1054 
[1m 74/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5262 - loss: 1.0900
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5300 - loss: 1.0785
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5322 - loss: 1.0736
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5339 - loss: 1.0693
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5358 - loss: 1.0654
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5371 - loss: 1.0628
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5379 - loss: 1.0613
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Epoch 41/53

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 1.1337
[1m 39/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5439 - loss: 1.0603 
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 388ms/step
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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 16ms/step
Saved model to disk.

Accuracy capturado en la ejecución 29: 51.26 [%]
F1-score capturado en la ejecución 29: 50.78 [%]

=== EJECUCIÓN 30 ===

--- TRAIN (ejecución 30) ---

--- TEST (ejecución 30) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           516 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 243,204 (950.02 KB)
 Trainable params: 243,204 (950.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 15ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m64/89[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 797us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 918us/step
Global accuracy score (validation) = 58.18 [%]
Global F1 score (validation) = 56.69 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.14986357 0.15224884 0.6580083  0.0398793 ]
 [0.22047962 0.2191727  0.5287705  0.03157713]
 [0.28555885 0.40543354 0.11668859 0.19231905]
 ...
 [0.15978906 0.1593387  0.59594977 0.08492244]
 [0.22420175 0.15995415 0.5076073  0.10823681]
 [0.16688193 0.13616486 0.6559413  0.0410119 ]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 58.41 [%]
Global accuracy score (test) = 52.17 [%]
Global F1 score (train) = 58.05 [%]
Global F1 score (test) = 51.38 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.38      0.27      0.32       400
MODERATE-INTENSITY       0.48      0.61      0.54       400
         SEDENTARY       0.54      0.72      0.62       400
VIGOROUS-INTENSITY       0.75      0.48      0.58       345

          accuracy                           0.52      1545
         macro avg       0.54      0.52      0.51      1545
      weighted avg       0.53      0.52      0.51      1545


Accuracy capturado en la ejecución 30: 52.17 [%]
F1-score capturado en la ejecución 30: 51.38 [%]

=== RESUMEN FINAL ===
Accuracies: [53.14, 51.97, 50.49, 50.36, 51.07, 51.13, 51.91, 49.45, 50.87, 52.17, 48.28, 51.46, 50.55, 51.59, 51.26, 53.07, 48.54, 51.33, 51.72, 51.91, 52.3, 46.86, 50.29, 52.3, 52.88, 52.62, 52.49, 50.49, 51.26, 52.17]
F1-scores: [53.13, 51.32, 49.64, 50.33, 50.24, 50.81, 51.43, 49.61, 50.36, 51.64, 48.86, 50.41, 50.32, 51.08, 50.17, 52.81, 48.02, 50.57, 51.59, 51.61, 52.09, 45.86, 50.52, 51.04, 52.45, 51.78, 52.33, 49.36, 50.78, 51.38]
Accuracy mean: 51.1977 | std: 1.4233
F1 mean: 50.7180 | std: 1.4428

Resultados guardados en /mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_M/case_M_ESANN_acc_superclasses_CPA_METs/metrics_test.npz
